# This is the Exploratory data analysis of the various causes of deaths in the world from 1990 t0 2019.
#Importing relevant libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
%matplotlib inline
#To show all hidden columns
pd.set_option('display.max_columns', None)
#Loading the dataset
df = pd.read_csv('Global_causes_of_deaths.csv')
#Showing the first two columns
df.head(2)
| Entity | Code | Year | Number of executions (Amnesty International) | Deaths - Meningitis - Sex: Both - Age: All Ages (Number) | Deaths - Neoplasms - Sex: Both - Age: All Ages (Number) | Deaths - Fire, heat, and hot substances - Sex: Both - Age: All Ages (Number) | Deaths - Malaria - Sex: Both - Age: All Ages (Number) | Deaths - Drowning - Sex: Both - Age: All Ages (Number) | Deaths - Interpersonal violence - Sex: Both - Age: All Ages (Number) | Deaths - HIV/AIDS - Sex: Both - Age: All Ages (Number) | Deaths - Drug use disorders - Sex: Both - Age: All Ages (Number) | Deaths - Tuberculosis - Sex: Both - Age: All Ages (Number) | Deaths - Road injuries - Sex: Both - Age: All Ages (Number) | Deaths - Maternal disorders - Sex: Both - Age: All Ages (Number) | Deaths - Lower respiratory infections - Sex: Both - Age: All Ages (Number) | Deaths - Neonatal disorders - Sex: Both - Age: All Ages (Number) | Deaths - Alcohol use disorders - Sex: Both - Age: All Ages (Number) | Deaths - Exposure to forces of nature - Sex: Both - Age: All Ages (Number) | Deaths - Diarrheal diseases - Sex: Both - Age: All Ages (Number) | Deaths - Environmental heat and cold exposure - Sex: Both - Age: All Ages (Number) | Deaths - Nutritional deficiencies - Sex: Both - Age: All Ages (Number) | Deaths - Self-harm - Sex: Both - Age: All Ages (Number) | Deaths - Conflict and terrorism - Sex: Both - Age: All Ages (Number) | Deaths - Diabetes mellitus - Sex: Both - Age: All Ages (Number) | Deaths - Poisonings - Sex: Both - Age: All Ages (Number) | Deaths - Protein-energy malnutrition - Sex: Both - Age: All Ages (Number) | Terrorism (deaths) | Deaths - Cardiovascular diseases - Sex: Both - Age: All Ages (Number) | Deaths - Chronic kidney disease - Sex: Both - Age: All Ages (Number) | Deaths - Chronic respiratory diseases - Sex: Both - Age: All Ages (Number) | Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: All Ages (Number) | Deaths - Digestive diseases - Sex: Both - Age: All Ages (Number) | Deaths - Acute hepatitis - Sex: Both - Age: All Ages (Number) | Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: All Ages (Number) | Deaths - Parkinson's disease - Sex: Both - Age: All Ages (Number) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | AFG | 2007 | 15 | 2933.0 | 15925.0 | 481.0 | 393.0 | 2127.0 | 3657.0 | 148.0 | 252.0 | 4995.0 | 7425.0 | 4990.0 | 27672.0 | 23890.0 | 111.0 | 296.0 | 9320.0 | 57.0 | 2488.0 | 1310.0 | 8220.0 | 3189.0 | 513.0 | 2439.0 | 1199.0 | 53962.0 | 4490.0 | 7222.0 | 3346.0 | 6458.0 | 3437.0 | 1402.0 | 450.0 |
| 1 | Afghanistan | AFG | 2008 | 17 | 2731.0 | 16148.0 | 462.0 | 255.0 | 1973.0 | 3785.0 | 157.0 | 261.0 | 4790.0 | 7355.0 | 5020.0 | 25800.0 | 23792.0 | 114.0 | 1317.0 | 8275.0 | 57.0 | 2277.0 | 1330.0 | 6895.0 | 3261.0 | 495.0 | 2231.0 | 1092.0 | 54051.0 | 4534.0 | 7143.0 | 3316.0 | 6408.0 | 3005.0 | 1424.0 | 455.0 |
#Renaming column headers
df.rename(columns = {'Entity': 'Country', 'Code': 'Country Code', 'Year': 'Year_of_Death',
'Number of executions (Amnesty International)':'Executions_Amty_Int',
'Deaths - Meningitis - Sex: Both - Age: All Ages (Number)': 'Meningitis',
'Deaths - Neoplasms - Sex: Both - Age: All Ages (Number)': 'Neoplasms',
'Deaths - Fire, heat, and hot substances - Sex: Both - Age: All Ages (Number)': 'Fire_heat_and_hot_substances',
'Deaths - Malaria - Sex: Both - Age: All Ages (Number)': 'Malaria',
'Deaths - Drowning - Sex: Both - Age: All Ages (Number)': 'Drowning',
'Deaths - Interpersonal violence - Sex: Both - Age: All Ages (Number)': 'Interpersonal_violence',
'Deaths - HIV/AIDS - Sex: Both - Age: All Ages (Number)': 'HIV/AIDS',
'Deaths - Drug use disorders - Sex: Both - Age: All Ages (Number)': 'Drug_use_disorders',
'Deaths - Tuberculosis - Sex: Both - Age: All Ages (Number)': 'Tuberculosis',
'Deaths - Road injuries - Sex: Both - Age: All Ages (Number)': 'Road_injuries',
'Deaths - Maternal disorders - Sex: Both - Age: All Ages (Number)': 'Maternal_disorders',
'Deaths - Lower respiratory infections - Sex: Both - Age: All Ages (Number)': 'Lower_respiratory_infections',
'Deaths - Neonatal disorders - Sex: Both - Age: All Ages (Number)': 'Neonatal_disorders',
'Deaths - Alcohol use disorders - Sex: Both - Age: All Ages (Number)': 'Alcohol_use_disorders',
'Deaths - Exposure to forces of nature - Sex: Both - Age: All Ages (Number': 'Exposure_to_forces_of_nature',
'Deaths - Diarrheal diseases - Sex: Both - Age: All Ages (Number)': 'Diarrheal_diseases',
'Deaths - Environmental heat and cold exposure - Sex: Both - Age: All Ages (Number)': 'Environmental_heat_and_cold_exposure',
'Deaths - Nutritional deficiencies - Sex: Both - Age: All Ages (Number)': 'Nutritional_deficiencies',
'Deaths - Self-harm - Sex: Both - Age: All Ages (Number)': 'Self-harm',
'Deaths - Conflict and terrorism - Sex: Both - Age: All Ages (Number)': 'Conflict_and_terrorism',
'Deaths - Diabetes mellitus - Sex: Both - Age: All Ages (Number': 'Diabetes_Mellitus',
'Deaths - Poisonings - Sex: Both - Age: All Ages (Number)': 'Poisonings',
'Deaths - Protein-energy malnutrition - Sex: Both - Age: All Ages (Number)': 'Protein-energy_malnutrition',
'Terrorism (deaths)': 'Terrorism',
'Deaths - Cardiovascular diseases - Sex: Both - Age: All Ages (Number)': 'Cardiovascular_diseases',
'Deaths - Chronic kidney disease - Sex: Both - Age: All Ages (Number)': 'Chronic_kidney_disease',
'Deaths - Chronic respiratory diseases - Sex: Both - Age: All Ages (Number)': 'Deaths - Chronic_Respiratory_diseases',
'Deaths - Cirrhosis and other chronic liver diseases - Sex: Both - Age: All Ages (Number)': 'Cirrhosis_liver_diseases',
'Deaths - Digestive diseases - Sex: Both - Age: All Ages (Number)': 'Digestive_diseases',
'Deaths - Acute hepatitis - Sex: Both - Age: All Ages (Number)': 'Acute_hepatitis',
"Deaths - Alzheimer's disease and other dementias - Sex: Both - Age: All Ages (Number)": 'Alzheimer disease',
"Deaths - Parkinson's disease - Sex: Both - Age: All Ages (Number)": 'Parkinson disease'}, inplace = True)
df.rename(columns = {'Deaths - Exposure to forces of nature - Sex: Both - Age: All Ages (Number)': 'Exposure_to_forces_of_nature',
'Deaths - Diabetes mellitus - Sex: Both - Age: All Ages (Number)': 'Diabetes_mellitus',
'Deaths - Chronic_Respiratory_diseases': 'Chronic_Respiratory_diseases'}, inplace = True)
#Creating a copy of the dataframe
new_df = df[:]
#To display the first five rows of the dataframe
new_df.head()
| Country | Country Code | Year_of_Death | Executions_Amty_Int | Meningitis | Neoplasms | Fire_heat_and_hot_substances | Malaria | Drowning | Interpersonal_violence | HIV/AIDS | Drug_use_disorders | Tuberculosis | Road_injuries | Maternal_disorders | Lower_respiratory_infections | Neonatal_disorders | Alcohol_use_disorders | Exposure_to_forces_of_nature | Diarrheal_diseases | Environmental_heat_and_cold_exposure | Nutritional_deficiencies | Self-harm | Conflict_and_terrorism | Diabetes_mellitus | Poisonings | Protein-energy_malnutrition | Terrorism | Cardiovascular_diseases | Chronic_kidney_disease | Chronic_Respiratory_diseases | Cirrhosis_liver_diseases | Digestive_diseases | Acute_hepatitis | Alzheimer disease | Parkinson disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | AFG | 2007 | 15 | 2933.0 | 15925.0 | 481.0 | 393.0 | 2127.0 | 3657.0 | 148.0 | 252.0 | 4995.0 | 7425.0 | 4990.0 | 27672.0 | 23890.0 | 111.0 | 296.0 | 9320.0 | 57.0 | 2488.0 | 1310.0 | 8220.0 | 3189.0 | 513.0 | 2439.0 | 1199.0 | 53962.0 | 4490.0 | 7222.0 | 3346.0 | 6458.0 | 3437.0 | 1402.0 | 450.0 |
| 1 | Afghanistan | AFG | 2008 | 17 | 2731.0 | 16148.0 | 462.0 | 255.0 | 1973.0 | 3785.0 | 157.0 | 261.0 | 4790.0 | 7355.0 | 5020.0 | 25800.0 | 23792.0 | 114.0 | 1317.0 | 8275.0 | 57.0 | 2277.0 | 1330.0 | 6895.0 | 3261.0 | 495.0 | 2231.0 | 1092.0 | 54051.0 | 4534.0 | 7143.0 | 3316.0 | 6408.0 | 3005.0 | 1424.0 | 455.0 |
| 2 | Afghanistan | AFG | 2009 | 0 | 2460.0 | 16383.0 | 448.0 | 239.0 | 1852.0 | 3874.0 | 167.0 | 270.0 | 4579.0 | 7290.0 | 5013.0 | 24340.0 | 23672.0 | 115.0 | 101.0 | 7359.0 | 57.0 | 2040.0 | 1342.0 | 7617.0 | 3336.0 | 483.0 | 1998.0 | 1065.0 | 53964.0 | 4597.0 | 7045.0 | 3291.0 | 6358.0 | 2663.0 | 1449.0 | 460.0 |
| 3 | Afghanistan | AFG | 2011 | 2 | 2327.0 | 17094.0 | 448.0 | 390.0 | 1775.0 | 4170.0 | 184.0 | 292.0 | 4259.0 | 7432.0 | 4857.0 | 22883.0 | 23951.0 | 120.0 | 83.0 | 6412.0 | 58.0 | 1846.0 | 1391.0 | 9142.0 | 3550.0 | 483.0 | 1805.0 | 1525.0 | 54347.0 | 4785.0 | 6916.0 | 3318.0 | 6370.0 | 2365.0 | 1508.0 | 473.0 |
| 4 | Afghanistan | AFG | 2012 | 14 | 2254.0 | 17522.0 | 445.0 | 94.0 | 1716.0 | 4245.0 | 191.0 | 305.0 | 4122.0 | 7494.0 | 4736.0 | 22162.0 | 24057.0 | 123.0 | 333.0 | 6008.0 | 103.0 | 1705.0 | 1413.0 | 11350.0 | 3682.0 | 482.0 | 1667.0 | 3521.0 | 54868.0 | 4846.0 | 6878.0 | 3353.0 | 6398.0 | 2264.0 | 1544.0 | 482.0 |
#To show the metadata of the dataframe
new_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 8254 entries, 0 to 8253 Data columns (total 36 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Country 8254 non-null object 1 Country Code 6206 non-null object 2 Year_of_Death 8254 non-null int64 3 Executions_Amty_Int 267 non-null object 4 Meningitis 8010 non-null float64 5 Neoplasms 8010 non-null float64 6 Fire_heat_and_hot_substances 8010 non-null float64 7 Malaria 8010 non-null float64 8 Drowning 8010 non-null float64 9 Interpersonal_violence 8010 non-null float64 10 HIV/AIDS 8010 non-null float64 11 Drug_use_disorders 8010 non-null float64 12 Tuberculosis 8010 non-null float64 13 Road_injuries 8010 non-null float64 14 Maternal_disorders 8010 non-null float64 15 Lower_respiratory_infections 8010 non-null float64 16 Neonatal_disorders 8010 non-null float64 17 Alcohol_use_disorders 8010 non-null float64 18 Exposure_to_forces_of_nature 8010 non-null float64 19 Diarrheal_diseases 8010 non-null float64 20 Environmental_heat_and_cold_exposure 8010 non-null float64 21 Nutritional_deficiencies 8010 non-null float64 22 Self-harm 8010 non-null float64 23 Conflict_and_terrorism 8010 non-null float64 24 Diabetes_mellitus 8010 non-null float64 25 Poisonings 8010 non-null float64 26 Protein-energy_malnutrition 8010 non-null float64 27 Terrorism 2891 non-null float64 28 Cardiovascular_diseases 8010 non-null float64 29 Chronic_kidney_disease 8010 non-null float64 30 Chronic_Respiratory_diseases 8010 non-null float64 31 Cirrhosis_liver_diseases 8010 non-null float64 32 Digestive_diseases 8010 non-null float64 33 Acute_hepatitis 8010 non-null float64 34 Alzheimer disease 8010 non-null float64 35 Parkinson disease 8010 non-null float64 dtypes: float64(32), int64(1), object(3) memory usage: 2.3+ MB
#A transposed statistical display of the dataset
new_df.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Year_of_Death | 8254.0 | 2004.448025 | 8.642230e+00 | 1990.0 | 1997.00 | 2004.0 | 2012.00 | 2019.0 |
| Meningitis | 8010.0 | 12909.701124 | 4.179939e+04 | 0.0 | 29.00 | 294.0 | 3187.75 | 432524.0 |
| Neoplasms | 8010.0 | 298398.509363 | 8.643901e+05 | 1.0 | 1934.25 | 10338.5 | 91869.25 | 10079637.0 |
| Fire_heat_and_hot_substances | 8010.0 | 4444.838077 | 1.211191e+04 | 0.0 | 35.00 | 244.0 | 1470.75 | 129705.0 |
| Malaria | 8010.0 | 31812.044569 | 1.230359e+05 | 0.0 | 0.00 | 1.0 | 2462.00 | 961129.0 |
| Drowning | 8010.0 | 12532.637953 | 4.009599e+04 | 0.0 | 58.00 | 393.5 | 3017.75 | 460665.0 |
| Interpersonal_violence | 8010.0 | 15315.848315 | 4.288854e+04 | 0.0 | 76.25 | 494.0 | 4372.50 | 463129.0 |
| HIV/AIDS | 8010.0 | 47251.428215 | 1.744798e+05 | 0.0 | 26.00 | 420.0 | 9484.50 | 1844490.0 |
| Drug_use_disorders | 8010.0 | 3469.958926 | 1.118651e+04 | 0.0 | 7.00 | 57.0 | 518.75 | 128083.0 |
| Tuberculosis | 8010.0 | 56055.270162 | 1.837876e+05 | 0.0 | 62.00 | 956.0 | 10377.75 | 1808478.0 |
| Road_injuries | 8010.0 | 44516.614232 | 1.269077e+05 | 0.0 | 332.25 | 1969.5 | 13236.00 | 1285039.0 |
| Maternal_disorders | 8010.0 | 9317.366417 | 3.066556e+04 | 0.0 | 8.00 | 163.0 | 1868.00 | 302586.0 |
| Lower_respiratory_infections | 8010.0 | 104292.072409 | 2.897865e+05 | 0.0 | 644.50 | 5872.0 | 37384.25 | 3320008.0 |
| Neonatal_disorders | 8010.0 | 92688.577528 | 2.947175e+05 | 0.0 | 199.00 | 2344.0 | 20780.25 | 3005945.0 |
| Alcohol_use_disorders | 8010.0 | 6106.161174 | 1.845507e+04 | 0.0 | 22.00 | 172.0 | 1246.25 | 181768.0 |
| Exposure_to_forces_of_nature | 8010.0 | 1708.418352 | 1.383435e+04 | 0.0 | 0.00 | 2.0 | 99.00 | 248861.0 |
| Diarrheal_diseases | 8010.0 | 81460.836080 | 2.843796e+05 | 0.0 | 49.00 | 992.5 | 13289.25 | 2904396.0 |
| Environmental_heat_and_cold_exposure | 8010.0 | 2237.055930 | 7.179057e+03 | 0.0 | 5.00 | 55.0 | 321.00 | 72653.0 |
| Nutritional_deficiencies | 8010.0 | 16586.057303 | 5.546863e+04 | 0.0 | 15.00 | 291.0 | 4639.00 | 757152.0 |
| Self-harm | 8010.0 | 30072.524469 | 8.722992e+04 | 0.0 | 181.00 | 995.0 | 7478.00 | 841164.0 |
| Conflict_and_terrorism | 8010.0 | 3763.654432 | 2.298289e+04 | 0.0 | 0.00 | 3.0 | 338.75 | 566518.0 |
| Diabetes_mellitus | 8010.0 | 39436.227840 | 1.109832e+05 | 1.0 | 409.00 | 1894.0 | 14308.25 | 1551170.0 |
| Poisonings | 8010.0 | 3189.111111 | 9.180095e+03 | 0.0 | 13.00 | 125.0 | 797.75 | 92101.0 |
| Protein-energy_malnutrition | 8010.0 | 14441.384519 | 4.798772e+04 | 0.0 | 10.00 | 233.5 | 4245.00 | 656314.0 |
| Terrorism | 2891.0 | 349.235905 | 1.917144e+03 | 0.0 | 0.00 | 5.0 | 60.00 | 44490.0 |
| Cardiovascular_diseases | 8010.0 | 567277.738951 | 1.606918e+06 | 4.0 | 4348.50 | 23265.5 | 166331.75 | 18562510.0 |
| Chronic_kidney_disease | 8010.0 | 36145.452934 | 1.028788e+05 | 0.0 | 281.00 | 1651.0 | 11921.75 | 1427232.0 |
| Chronic_Respiratory_diseases | 8010.0 | 131501.249189 | 4.174924e+05 | 1.0 | 526.25 | 2960.5 | 28156.50 | 3974315.0 |
| Cirrhosis_liver_diseases | 8010.0 | 46686.335206 | 1.282383e+05 | 0.0 | 304.00 | 2134.0 | 16802.25 | 1472012.0 |
| Digestive_diseases | 8010.0 | 82614.909613 | 2.253554e+05 | 0.0 | 599.00 | 4032.5 | 28388.75 | 2557689.0 |
| Acute_hepatitis | 8010.0 | 4586.226592 | 1.669243e+04 | 0.0 | 3.00 | 47.0 | 453.75 | 166405.0 |
| Alzheimer disease | 8010.0 | 39233.946567 | 1.179772e+05 | 0.0 | 201.00 | 1337.0 | 11867.75 | 1623276.0 |
| Parkinson disease | 8010.0 | 9367.016979 | 2.735872e+04 | 0.0 | 55.00 | 331.0 | 2954.00 | 362907.0 |
new_df.shape
(8254, 36)
#To display all columns with null values
new_df.isnull().sum()
Country 0 Country Code 2048 Year_of_Death 0 Executions_Amty_Int 7987 Meningitis 244 Neoplasms 244 Fire_heat_and_hot_substances 244 Malaria 244 Drowning 244 Interpersonal_violence 244 HIV/AIDS 244 Drug_use_disorders 244 Tuberculosis 244 Road_injuries 244 Maternal_disorders 244 Lower_respiratory_infections 244 Neonatal_disorders 244 Alcohol_use_disorders 244 Exposure_to_forces_of_nature 244 Diarrheal_diseases 244 Environmental_heat_and_cold_exposure 244 Nutritional_deficiencies 244 Self-harm 244 Conflict_and_terrorism 244 Diabetes_mellitus 244 Poisonings 244 Protein-energy_malnutrition 244 Terrorism 5363 Cardiovascular_diseases 244 Chronic_kidney_disease 244 Chronic_Respiratory_diseases 244 Cirrhosis_liver_diseases 244 Digestive_diseases 244 Acute_hepatitis 244 Alzheimer disease 244 Parkinson disease 244 dtype: int64
# To dispaly all integer columns
new_df_int = []
for x in new_df.dtypes.index:
if new_df.dtypes[x] == 'int64':
new_df_int.append(x)
new_df_int
['Year_of_Death']
# To dispaly all float64 columns
new_df_int = []
for x in new_df.dtypes.index:
if new_df.dtypes[x] == 'float':
new_df_int.append(x)
new_df_int
['Meningitis', 'Neoplasms', 'Fire_heat_and_hot_substances', 'Malaria', 'Drowning', 'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders', 'Tuberculosis', 'Road_injuries', 'Maternal_disorders', 'Lower_respiratory_infections', 'Neonatal_disorders', 'Alcohol_use_disorders', 'Exposure_to_forces_of_nature', 'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure', 'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism', 'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition', 'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease', 'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases', 'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease', 'Parkinson disease']
# To dispaly all object columns
new_df_int = []
for x in new_df.dtypes.index:
if new_df.dtypes[x] == 'object':
new_df_int.append(x)
new_df_int
['Country', 'Country Code', 'Executions_Amty_Int']
#Dropping two columns
new_df.drop(columns = ['Executions_Amty_Int', 'Country Code'], inplace = True)
#Replacing a null values with the mean of the dataframe
new_df.fillna(new_df.mean(),inplace = True)
C:\Users\user\AppData\Local\Temp/ipykernel_8456/4268064738.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction. new_df.fillna(new_df.mean(),inplace = True)
#To verify there are no other null values in the dataframe
new_df.isna().sum()
Country 0 Year_of_Death 0 Meningitis 0 Neoplasms 0 Fire_heat_and_hot_substances 0 Malaria 0 Drowning 0 Interpersonal_violence 0 HIV/AIDS 0 Drug_use_disorders 0 Tuberculosis 0 Road_injuries 0 Maternal_disorders 0 Lower_respiratory_infections 0 Neonatal_disorders 0 Alcohol_use_disorders 0 Exposure_to_forces_of_nature 0 Diarrheal_diseases 0 Environmental_heat_and_cold_exposure 0 Nutritional_deficiencies 0 Self-harm 0 Conflict_and_terrorism 0 Diabetes_mellitus 0 Poisonings 0 Protein-energy_malnutrition 0 Terrorism 0 Cardiovascular_diseases 0 Chronic_kidney_disease 0 Chronic_Respiratory_diseases 0 Cirrhosis_liver_diseases 0 Digestive_diseases 0 Acute_hepatitis 0 Alzheimer disease 0 Parkinson disease 0 dtype: int64
new_df.pivot_table(index = 'Year_of_Death', values = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning', 'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'], aggfunc = 'sum')
| Acute_hepatitis | Alcohol_use_disorders | Alzheimer disease | Cardiovascular_diseases | Chronic_Respiratory_diseases | Chronic_kidney_disease | Cirrhosis_liver_diseases | Conflict_and_terrorism | Diabetes_mellitus | Diarrheal_diseases | Digestive_diseases | Drowning | Drug_use_disorders | Environmental_heat_and_cold_exposure | Exposure_to_forces_of_nature | Fire_heat_and_hot_substances | HIV/AIDS | Interpersonal_violence | Lower_respiratory_infections | Malaria | Maternal_disorders | Meningitis | Neonatal_disorders | Neoplasms | Nutritional_deficiencies | Parkinson disease | Poisonings | Protein-energy_malnutrition | Road_injuries | Self-harm | Terrorism | Tuberculosis | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year_of_Death | ||||||||||||||||||||||||||||||||
| 1990 | 1.661965e+06 | 1.249543e+06 | 6.393869e+06 | 1.308505e+08 | 3.258818e+07 | 6.421137e+06 | 1.067155e+07 | 1.110766e+06 | 7.158245e+06 | 2.925154e+07 | 1.964008e+07 | 4.621206e+06 | 6.145225e+05 | 576158.615231 | 4.372296e+05 | 1.265927e+06 | 4.007790e+06 | 3.796059e+06 | 3.393282e+07 | 8.758178e+06 | 3.023030e+06 | 4.370912e+06 | 2.994935e+07 | 6.368086e+07 | 7.482045e+06 | 1.651046e+06 | 898864.222222 | 6.471534e+06 | 1.151764e+07 | 7.875844e+06 | 84655.698720 | 1.797335e+07 |
| 1991 | 1.657380e+06 | 1.316474e+06 | 6.673846e+06 | 1.328985e+08 | 3.327618e+07 | 6.581273e+06 | 1.086276e+07 | 8.387149e+05 | 7.382670e+06 | 2.943951e+07 | 1.995623e+07 | 4.575059e+06 | 6.770935e+05 | 593810.671161 | 1.436020e+06 | 1.278711e+06 | 5.023239e+06 | 3.912932e+06 | 3.371600e+07 | 8.979938e+06 | 2.993140e+06 | 4.350372e+06 | 2.976181e+07 | 6.515604e+07 | 7.237208e+06 | 1.698855e+06 | 900314.333333 | 6.249857e+06 | 1.159941e+07 | 8.042053e+06 | 76624.677966 | 1.813422e+07 |
| 1992 | 1.646589e+06 | 1.414127e+06 | 6.955305e+06 | 1.354871e+08 | 3.399616e+07 | 6.778986e+06 | 1.106688e+07 | 6.285105e+05 | 7.643306e+06 | 2.921887e+07 | 2.029752e+07 | 4.514849e+06 | 7.311735e+05 | 628736.727091 | 1.362854e+05 | 1.292700e+06 | 6.185702e+06 | 4.153043e+06 | 3.360680e+07 | 9.003701e+06 | 3.010785e+06 | 4.341994e+06 | 2.961701e+07 | 6.674348e+07 | 6.978514e+06 | 1.748998e+06 | 909005.444444 | 6.022944e+06 | 1.171822e+07 | 8.245211e+06 | 73229.723971 | 1.840361e+07 |
| 1993 | 1.569312e+06 | 1.456631e+06 | 6.697687e+06 | 1.316369e+08 | 3.287207e+07 | 6.477967e+06 | 1.070366e+07 | 5.721000e+05 | 7.384169e+06 | 2.743239e+07 | 1.957938e+07 | 4.335471e+06 | 7.349480e+05 | 668640.000000 | 2.031900e+05 | 1.259350e+06 | 6.841321e+06 | 4.199189e+06 | 3.197443e+07 | 8.656775e+06 | 2.830872e+06 | 4.118829e+06 | 2.816487e+07 | 6.459464e+07 | 6.507485e+06 | 1.682132e+06 | 882970.000000 | 5.622720e+06 | 1.124102e+07 | 8.064378e+06 | 93245.986510 | 1.733862e+07 |
| 1994 | 1.591217e+06 | 1.617405e+06 | 7.330315e+06 | 1.393954e+08 | 3.448724e+07 | 7.019021e+06 | 1.138206e+07 | 6.143654e+06 | 8.005891e+06 | 2.768482e+07 | 2.068427e+07 | 4.439960e+06 | 8.222436e+05 | 745401.559301 | 1.379752e+05 | 1.325004e+06 | 8.701827e+06 | 4.443971e+06 | 3.265090e+07 | 8.916309e+06 | 2.919340e+06 | 4.178250e+06 | 2.882937e+07 | 6.887554e+07 | 6.437942e+06 | 1.817816e+06 | 923220.111111 | 5.576330e+06 | 1.183842e+07 | 8.618473e+06 | 67772.195780 | 1.769225e+07 |
| 1995 | 1.574135e+06 | 1.651731e+06 | 7.610644e+06 | 1.409082e+08 | 3.478288e+07 | 7.239235e+06 | 1.157666e+07 | 6.763032e+05 | 8.263913e+06 | 2.711182e+07 | 2.093490e+07 | 4.391703e+06 | 8.594585e+05 | 750927.615231 | 2.084336e+05 | 1.317046e+06 | 1.016770e+07 | 4.470921e+06 | 3.241442e+07 | 9.021580e+06 | 2.901961e+06 | 4.151242e+06 | 2.870609e+07 | 7.026983e+07 | 7.098007e+06 | 1.874415e+06 | 920443.222222 | 6.271585e+06 | 1.196564e+07 | 8.726806e+06 | 67202.026634 | 1.760259e+07 |
| 1996 | 1.537720e+06 | 1.648099e+06 | 7.840580e+06 | 1.416507e+08 | 3.508088e+07 | 7.448282e+06 | 1.168176e+07 | 8.476532e+05 | 8.518086e+06 | 2.656425e+07 | 2.104785e+07 | 4.252830e+06 | 8.779755e+05 | 700090.615231 | 1.623596e+05 | 1.299951e+06 | 1.142593e+07 | 4.349887e+06 | 3.187989e+07 | 9.126420e+06 | 2.875882e+06 | 4.236686e+06 | 2.840057e+07 | 7.115734e+07 | 6.619371e+06 | 1.925851e+06 | 905442.222222 | 5.830991e+06 | 1.195681e+07 | 8.664227e+06 | 70489.498443 | 1.743498e+07 |
| 1997 | 1.519494e+06 | 1.633219e+06 | 8.017137e+06 | 1.422615e+08 | 3.535266e+07 | 7.679628e+06 | 1.177324e+07 | 9.533525e+05 | 8.770191e+06 | 2.614563e+07 | 2.116454e+07 | 4.145914e+06 | 8.851156e+05 | 660825.559301 | 1.814962e+05 | 1.289603e+06 | 1.253207e+07 | 4.282593e+06 | 3.137905e+07 | 9.305505e+06 | 2.886995e+06 | 4.054495e+06 | 2.809002e+07 | 7.184094e+07 | 6.368869e+06 | 1.973769e+06 | 887887.111111 | 5.610298e+06 | 1.198785e+07 | 8.661926e+06 | 83411.206157 | 1.744728e+07 |
| 1998 | 1.488658e+06 | 1.630028e+06 | 8.161538e+06 | 1.422553e+08 | 3.529417e+07 | 7.885472e+06 | 1.180409e+07 | 1.030349e+06 | 8.957464e+06 | 2.565834e+07 | 2.116481e+07 | 4.062067e+06 | 9.052717e+05 | 667525.447441 | 4.385733e+05 | 1.266444e+06 | 1.379532e+07 | 4.292973e+06 | 3.072340e+07 | 9.326665e+06 | 2.869754e+06 | 3.957349e+06 | 2.768783e+07 | 7.245530e+07 | 6.071071e+06 | 2.015003e+06 | 874906.888889 | 5.359881e+06 | 1.197872e+07 | 8.674909e+06 | 79720.350052 | 1.733040e+07 |
| 1999 | 1.464686e+06 | 1.673449e+06 | 8.399478e+06 | 1.447198e+08 | 3.548634e+07 | 8.154977e+06 | 1.197172e+07 | 1.334037e+06 | 9.208637e+06 | 2.520534e+07 | 2.140567e+07 | 3.965088e+06 | 9.301717e+05 | 652485.447441 | 6.429963e+05 | 1.277764e+06 | 1.523411e+07 | 4.375273e+06 | 3.026056e+07 | 9.261324e+06 | 2.868104e+06 | 3.925535e+06 | 2.743691e+07 | 7.403033e+07 | 5.807843e+06 | 2.082333e+06 | 881250.888889 | 5.145720e+06 | 1.214743e+07 | 8.781565e+06 | 70596.811484 | 1.728829e+07 |
| 2000 | 1.446091e+06 | 1.740665e+06 | 8.737762e+06 | 1.480113e+08 | 3.604749e+07 | 8.524581e+06 | 1.224904e+07 | 1.135916e+06 | 9.550049e+06 | 2.482737e+07 | 2.182162e+07 | 3.897650e+06 | 9.620536e+05 | 677036.559301 | 1.033542e+05 | 1.293796e+06 | 1.660532e+07 | 4.492312e+06 | 2.992586e+07 | 9.206590e+06 | 2.870678e+06 | 3.914298e+06 | 2.736994e+07 | 7.606776e+07 | 5.599695e+06 | 2.172943e+06 | 902435.111111 | 4.967193e+06 | 1.240298e+07 | 8.821783e+06 | 71182.160152 | 1.736304e+07 |
| 2001 | 1.403053e+06 | 1.782497e+06 | 8.989578e+06 | 1.495699e+08 | 3.607339e+07 | 8.769196e+06 | 1.242373e+07 | 6.534649e+05 | 9.798067e+06 | 2.415664e+07 | 2.202179e+07 | 3.771793e+06 | 9.361156e+05 | 687579.503371 | 3.028338e+05 | 1.287404e+06 | 1.766627e+07 | 4.481228e+06 | 2.915766e+07 | 9.493590e+06 | 2.817243e+06 | 3.842552e+06 | 2.700187e+07 | 7.689816e+07 | 5.333007e+06 | 2.243045e+06 | 904217.000000 | 4.733175e+06 | 1.246854e+07 | 8.604734e+06 | 81160.160152 | 1.699420e+07 |
| 2002 | 1.352192e+06 | 1.828438e+06 | 9.314889e+06 | 1.525915e+08 | 3.632446e+07 | 9.083340e+06 | 1.269217e+07 | 6.270769e+05 | 1.015371e+07 | 2.352020e+07 | 2.238626e+07 | 3.636232e+06 | 9.119396e+05 | 719415.503371 | 9.812677e+04 | 1.278814e+06 | 1.858488e+07 | 4.573952e+06 | 2.875622e+07 | 9.654239e+06 | 2.756833e+06 | 3.769457e+06 | 2.682721e+07 | 7.831447e+07 | 5.094432e+06 | 2.334243e+06 | 910720.000000 | 4.532484e+06 | 1.258816e+07 | 8.508447e+06 | 79722.114147 | 1.667683e+07 |
| 2003 | 1.305056e+06 | 1.865017e+06 | 9.641248e+06 | 1.545311e+08 | 3.634466e+07 | 9.364362e+06 | 1.292875e+07 | 6.629229e+05 | 1.044869e+07 | 2.284575e+07 | 2.268449e+07 | 3.498296e+06 | 8.887496e+05 | 742497.503371 | 3.116288e+05 | 1.282700e+06 | 1.927681e+07 | 4.441960e+06 | 2.834549e+07 | 9.942037e+06 | 2.690640e+06 | 3.738645e+06 | 2.663623e+07 | 7.967422e+07 | 4.173177e+06 | 2.410932e+06 | 918305.000000 | 3.651981e+06 | 1.269105e+07 | 8.542525e+06 | 75258.114147 | 1.631525e+07 |
| 2004 | 1.274595e+06 | 1.869142e+06 | 9.894721e+06 | 1.534706e+08 | 3.577863e+07 | 9.495341e+06 | 1.297099e+07 | 6.455126e+05 | 1.053445e+07 | 2.194370e+07 | 2.264725e+07 | 3.383649e+06 | 8.791027e+05 | 687161.391511 | 2.445689e+06 | 1.252563e+06 | 1.958029e+07 | 4.359170e+06 | 2.764228e+07 | 9.877015e+06 | 2.633312e+06 | 3.659508e+06 | 2.623107e+07 | 8.021770e+07 | 3.916311e+06 | 2.447128e+06 | 920138.777778 | 3.430216e+06 | 1.269446e+07 | 8.525595e+06 | 89520.832238 | 1.567580e+07 |
| 2005 | 1.291444e+06 | 1.900321e+06 | 1.039329e+07 | 1.575186e+08 | 3.634158e+07 | 9.941602e+06 | 1.338061e+07 | 5.490365e+05 | 1.097457e+07 | 2.179459e+07 | 2.327692e+07 | 3.367305e+06 | 9.231846e+05 | 706402.559301 | 8.285672e+05 | 1.271783e+06 | 1.964079e+07 | 4.358761e+06 | 2.777185e+07 | 9.767443e+06 | 2.653198e+06 | 3.683544e+06 | 2.625414e+07 | 8.261733e+07 | 3.799404e+06 | 2.566524e+06 | 936083.111111 | 3.321915e+06 | 1.292725e+07 | 8.683788e+06 | 87792.473193 | 1.559020e+07 |
| 2006 | 1.248982e+06 | 1.849987e+06 | 1.069656e+07 | 1.562081e+08 | 3.589888e+07 | 1.012354e+07 | 1.334632e+07 | 6.572452e+05 | 1.108847e+07 | 2.149156e+07 | 2.320262e+07 | 3.218064e+06 | 9.224287e+05 | 665131.447441 | 2.047783e+05 | 1.235509e+06 | 1.890123e+07 | 4.239392e+06 | 2.725773e+07 | 9.720416e+06 | 2.585376e+06 | 3.636044e+06 | 2.574857e+07 | 8.272365e+07 | 3.632662e+06 | 2.612923e+06 | 908342.888889 | 3.165333e+06 | 1.281269e+07 | 8.503527e+06 | 94844.057765 | 1.515347e+07 |
| 2007 | 1.217801e+06 | 1.825958e+06 | 1.118838e+07 | 1.584827e+08 | 3.622814e+07 | 1.048454e+07 | 1.355200e+07 | 6.420895e+05 | 1.137690e+07 | 2.134275e+07 | 2.352770e+07 | 3.165186e+06 | 9.359986e+05 | 620949.559301 | 1.671282e+05 | 1.228440e+06 | 1.797353e+07 | 4.192174e+06 | 2.718860e+07 | 9.772339e+06 | 2.537653e+06 | 3.602614e+06 | 2.557652e+07 | 8.462961e+07 | 3.523017e+06 | 2.712474e+06 | 901256.111111 | 3.060479e+06 | 1.294596e+07 | 8.447368e+06 | 103429.878243 | 1.497545e+07 |
| 2008 | 1.186935e+06 | 1.803678e+06 | 1.163707e+07 | 1.612115e+08 | 3.666256e+07 | 1.083906e+07 | 1.373251e+07 | 6.137695e+05 | 1.166276e+07 | 2.103084e+07 | 2.381166e+07 | 3.097459e+06 | 9.486146e+05 | 600800.559301 | 2.228889e+06 | 1.214602e+06 | 1.682126e+07 | 4.212860e+06 | 2.704886e+07 | 9.718697e+06 | 2.491211e+06 | 3.512374e+06 | 2.525785e+07 | 8.641431e+07 | 3.409644e+06 | 2.815241e+06 | 903574.111111 | 2.951052e+06 | 1.303947e+07 | 8.399150e+06 | 87539.575579 | 1.475146e+07 |
| 2009 | 1.132843e+06 | 1.744117e+06 | 1.201470e+07 | 1.614772e+08 | 3.627802e+07 | 1.104654e+07 | 1.363317e+07 | 6.824132e+05 | 1.177254e+07 | 2.020299e+07 | 2.363852e+07 | 2.995919e+06 | 9.426537e+05 | 567654.447441 | 1.186943e+05 | 1.185314e+06 | 1.560973e+07 | 4.169610e+06 | 2.652408e+07 | 9.503830e+06 | 2.435246e+06 | 3.426390e+06 | 2.468162e+07 | 8.722632e+07 | 3.255591e+06 | 2.872161e+06 | 889897.888889 | 2.814886e+06 | 1.291021e+07 | 8.223743e+06 | 90332.226911 | 1.422411e+07 |
| 2010 | 1.103009e+06 | 1.721060e+06 | 1.249982e+07 | 1.642387e+08 | 3.639871e+07 | 1.138289e+07 | 1.372950e+07 | 5.292852e+05 | 1.199310e+07 | 1.973709e+07 | 2.381923e+07 | 2.939151e+06 | 9.543587e+05 | 606374.447441 | 1.987123e+06 | 1.176412e+06 | 1.467533e+07 | 4.119327e+06 | 2.627671e+07 | 9.401187e+06 | 2.392538e+06 | 3.324390e+06 | 2.423807e+07 | 8.882902e+07 | 3.246251e+06 | 2.958818e+06 | 887901.888889 | 2.810585e+06 | 1.287015e+07 | 8.207513e+06 | 89137.350052 | 1.382533e+07 |
| 2011 | 1.073792e+06 | 1.692581e+06 | 1.297180e+07 | 1.662087e+08 | 3.670323e+07 | 1.174337e+07 | 1.380419e+07 | 5.723632e+05 | 1.230503e+07 | 1.946982e+07 | 2.399849e+07 | 2.845752e+06 | 9.690417e+05 | 535962.447441 | 3.429183e+05 | 1.160212e+06 | 1.381655e+07 | 4.083633e+06 | 2.611534e+07 | 8.980200e+06 | 2.342251e+06 | 3.186528e+06 | 2.380662e+07 | 9.023874e+07 | 3.173359e+06 | 3.045024e+06 | 872625.888889 | 2.739772e+06 | 1.274892e+07 | 8.132530e+06 | 89695.878243 | 1.349532e+07 |
| 2012 | 1.050203e+06 | 1.667714e+06 | 1.341407e+07 | 1.676963e+08 | 3.677806e+07 | 1.204165e+07 | 1.380502e+07 | 9.636986e+05 | 1.264148e+07 | 1.872744e+07 | 2.404462e+07 | 2.780783e+06 | 9.842797e+05 | 531958.391511 | 9.566293e+04 | 1.138845e+06 | 1.285580e+07 | 4.088396e+06 | 2.601730e+07 | 8.304909e+06 | 2.258981e+06 | 3.052961e+06 | 2.330111e+07 | 9.139334e+07 | 3.010665e+06 | 3.126003e+06 | 854664.777778 | 2.581216e+06 | 1.257483e+07 | 7.997492e+06 | 108305.755102 | 1.330962e+07 |
| 2013 | 9.981518e+05 | 1.668957e+06 | 1.393847e+07 | 1.706409e+08 | 3.743920e+07 | 1.245557e+07 | 1.389226e+07 | 1.023420e+06 | 1.307296e+07 | 1.834886e+07 | 2.431654e+07 | 2.707591e+06 | 1.027975e+06 | 515092.447441 | 2.655973e+05 | 1.141500e+06 | 1.211971e+07 | 4.081944e+06 | 2.624264e+07 | 7.795629e+06 | 2.230907e+06 | 2.960438e+06 | 2.300817e+07 | 9.314492e+07 | 2.964201e+06 | 3.240934e+06 | 848172.888889 | 2.538171e+06 | 1.238418e+07 | 7.909681e+06 | 125141.396057 | 1.326555e+07 |
| 2014 | 9.376728e+05 | 1.674131e+06 | 1.444180e+07 | 1.728338e+08 | 3.784308e+07 | 1.278522e+07 | 1.392697e+07 | 1.626829e+06 | 1.348873e+07 | 1.772904e+07 | 2.445953e+07 | 2.626487e+06 | 1.080419e+06 | 506762.447441 | 8.380335e+04 | 1.124477e+06 | 1.155184e+07 | 4.061761e+06 | 2.620417e+07 | 7.532065e+06 | 2.145691e+06 | 2.873670e+06 | 2.241344e+07 | 9.477012e+07 | 2.897287e+06 | 3.336769e+06 | 832759.888889 | 2.475561e+06 | 1.220137e+07 | 7.798169e+06 | 190046.216534 | 1.314262e+07 |
| 2015 | 8.969278e+05 | 1.688641e+06 | 1.497279e+07 | 1.765291e+08 | 3.825057e+07 | 1.318309e+07 | 1.413279e+07 | 1.449403e+06 | 1.393661e+07 | 1.726607e+07 | 2.485794e+07 | 2.584297e+06 | 1.148310e+06 | 535081.447441 | 1.398883e+05 | 1.125022e+06 | 1.111296e+07 | 4.088260e+06 | 2.623585e+07 | 7.315673e+06 | 2.081780e+06 | 2.747332e+06 | 2.189841e+07 | 9.692326e+07 | 2.843148e+06 | 3.429082e+06 | 822652.888889 | 2.426802e+06 | 1.207455e+07 | 7.771952e+06 | 173484.452439 | 1.287277e+07 |
| 2016 | 8.522226e+05 | 1.696197e+06 | 1.551580e+07 | 1.789867e+08 | 3.861080e+07 | 1.351894e+07 | 1.426855e+07 | 1.370596e+06 | 1.434866e+07 | 1.679557e+07 | 2.511758e+07 | 2.518982e+06 | 1.227278e+06 | 492241.391511 | 9.431993e+04 | 1.117217e+06 | 1.074192e+07 | 4.046698e+06 | 2.597101e+07 | 6.860928e+06 | 2.027220e+06 | 2.671863e+06 | 2.099461e+07 | 9.864107e+07 | 2.771400e+06 | 3.509369e+06 | 809336.777778 | 2.361482e+06 | 1.195128e+07 | 7.740496e+06 | 158046.093393 | 1.263867e+07 |
| 2017 | 8.250854e+05 | 1.695006e+06 | 1.606750e+07 | 1.812624e+08 | 3.902449e+07 | 1.373698e+07 | 1.443222e+07 | 1.118332e+06 | 1.471771e+07 | 1.663643e+07 | 2.541115e+07 | 2.438215e+06 | 1.283179e+06 | 477866.335581 | 1.292675e+05 | 1.106364e+06 | 1.019817e+07 | 4.077815e+06 | 2.559143e+07 | 6.524143e+06 | 1.973958e+06 | 2.548515e+06 | 2.008372e+07 | 1.002749e+08 | 2.697389e+06 | 3.586867e+06 | 791345.666667 | 2.292121e+06 | 1.185473e+07 | 7.714167e+06 | 134514.272916 | 1.240577e+07 |
| 2018 | 7.823130e+05 | 1.685875e+06 | 1.643764e+07 | 1.821855e+08 | 3.915169e+07 | 1.387856e+07 | 1.436280e+07 | 8.765680e+05 | 1.493975e+07 | 1.561673e+07 | 2.528801e+07 | 2.348988e+06 | 1.312393e+06 | 464640.000000 | 1.092820e+05 | 1.094495e+06 | 9.337500e+06 | 4.008793e+06 | 2.497413e+07 | 6.367998e+06 | 1.908483e+06 | 2.387470e+06 | 1.883323e+07 | 1.011568e+08 | 2.508095e+06 | 3.654087e+06 | 765508.000000 | 2.120805e+06 | 1.166577e+07 | 7.648006e+06 | 93245.986510 | 1.180568e+07 |
| 2019 | 7.651880e+05 | 1.709565e+06 | 1.698870e+07 | 1.865919e+08 | 4.001690e+07 | 1.426023e+07 | 1.459103e+07 | 5.298210e+05 | 1.540983e+07 | 1.518179e+07 | 2.569630e+07 | 2.318444e+06 | 1.364993e+06 | 479449.000000 | 5.917300e+04 | 1.099726e+06 | 9.028405e+06 | 3.962124e+06 | 2.504127e+07 | 6.481284e+06 | 1.892483e+06 | 2.332419e+06 | 1.824529e+07 | 1.040213e+08 | 2.444227e+06 | 3.770569e+06 | 758680.000000 | 2.062098e+06 | 1.168243e+07 | 7.682558e+06 | 93245.986510 | 1.155348e+07 |
new_df.groupby(['Year_of_Death']).sum()['Meningitis'].sort_values(ascending = False)
Year_of_Death 1990 4370911.7124 1991 4350372.4135 1992 4341994.1146 1996 4236685.7124 1994 4178250.0112 1995 4151241.7124 1993 4118829.0000 1997 4054495.0112 1998 3957348.6090 1999 3925534.6090 2000 3914298.0112 2001 3842552.3101 2002 3769457.3101 2003 3738645.3101 2005 3683544.0112 2004 3659507.9079 2006 3636043.6090 2007 3602614.0112 2008 3512374.0112 2009 3426389.6090 2010 3324389.6090 2011 3186527.6090 2012 3052960.9079 2013 2960437.6090 2014 2873669.6090 2015 2747331.6090 2016 2671862.9079 2017 2548515.2067 2018 2387470.0000 2019 2332419.0000 Name: Meningitis, dtype: float64
pd.options.display.float_format = '{:.4f}'.format
sns.barplot(data = new_df, x = 'Year_of_Death', y = 'Meningitis')
sns.set(rc = {'figure.figsize':(25,16)})
import plotly.express as px
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Meningitis', color_discrete_sequence = ['Gold', 'Silver', 'Brown'])
fig.show()
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Meningitis', color = 'Meningitis')
fig.show()
#To confirm the corrctness of the plot
new_df[new_df['Year_of_Death'] == 2019]['Meningitis'].sum()
2332419.0
new_df.groupby(['Year_of_Death']).sum()['Neoplasms'].sort_values(ascending = False)
Year_of_Death 2019 104021332.0000 2018 101156797.0000 2017 100274878.0562 2016 98641069.5655 2015 96923257.0749 2014 94770121.0749 2013 93144920.0749 2012 91393340.5655 2011 90238742.0749 2010 88829022.0749 2009 87226316.0749 2008 86414311.0936 2007 84629606.0936 2006 82723654.0749 2005 82617330.0936 2004 80217695.5655 2003 79674216.5843 2002 78314470.5843 2001 76898160.5843 2000 76067762.0936 1999 74030326.0749 1998 72455300.0749 1997 71840940.0936 1996 71157339.6030 1995 70269834.6030 1994 68875535.0936 1992 66743476.6217 1991 65156037.1124 1993 64594642.0000 1990 63680862.6030 Name: Neoplasms, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Neoplasms', color = 'Neoplasms')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Fire_heat_and_hot_substances'].sort_values(ascending = False)
Year_of_Death 1994 1325004.3808 1995 1317046.2189 1996 1299951.2189 2000 1293796.3808 1992 1292699.8950 1997 1289603.3808 2001 1287403.5427 2003 1282699.5427 2002 1278813.5427 1991 1278711.0569 1999 1277763.7046 2005 1271783.3808 1998 1266443.7046 1990 1265927.2189 1993 1259350.0000 2004 1252562.8665 2006 1235508.7046 2007 1228440.3808 2008 1214602.3808 2009 1185313.7046 2010 1176411.7046 2011 1160211.7046 2013 1141499.7046 2012 1138844.8665 2015 1125021.7046 2014 1124476.7046 2016 1117216.8665 2017 1106364.0285 2019 1099726.0000 2018 1094495.0000 Name: Fire_heat_and_hot_substances, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Fire_heat_and_hot_substances', color = 'Fire_heat_and_hot_substances')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Malaria'].sort_values(ascending = False)
Year_of_Death 2003 9942037.4011 2004 9877015.3120 2007 9772339.4457 2005 9767443.4457 2006 9720416.3566 2008 9718697.4457 2002 9654239.4011 2009 9503830.3566 2001 9493590.4011 2010 9401187.3566 1998 9326665.3566 1997 9305505.4457 1999 9261324.3566 2000 9206590.4457 1996 9126420.4903 1995 9021580.4903 1992 9003700.5794 2011 8980200.3566 1991 8979937.5348 1994 8916309.4457 1990 8758178.4903 1993 8656775.0000 2012 8304909.3120 2013 7795629.3566 2014 7532065.3566 2015 7315673.3566 2016 6860928.3120 2017 6524143.2674 2019 6481284.0000 2018 6367998.0000 Name: Malaria, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Malaria', color = 'Malaria')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Drowning'].sort_values(ascending = False)
Year_of_Death 1990 4621206.0175 1991 4575058.6554 1992 4514849.2934 1994 4439960.3795 1995 4391703.0175 1993 4335471.0000 1996 4252830.0175 1997 4145914.3795 1998 4062067.1036 1999 3965088.1036 2000 3897650.3795 2001 3771792.7416 2002 3636231.7416 2003 3498295.7416 2004 3383649.4657 2005 3367305.3795 2006 3218064.1036 2007 3165186.3795 2008 3097459.3795 2009 2995919.1036 2010 2939151.1036 2011 2845752.1036 2012 2780783.4657 2013 2707591.1036 2014 2626487.1036 2015 2584297.1036 2016 2518982.4657 2017 2438214.8277 2018 2348988.0000 2019 2318444.0000 Name: Drowning, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Drowning', color = 'Drowning')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Interpersonal_violence'].sort_values(ascending = False)
Year_of_Death 2002 4573951.6348 2000 4492312.4831 2001 4481227.6348 1995 4470921.3315 1994 4443971.4831 2003 4441959.6348 1999 4375272.7865 2004 4359169.9382 2005 4358761.4831 1996 4349887.3315 1998 4292972.7865 1997 4282593.4831 2006 4239391.7865 2008 4212860.4831 1993 4199189.0000 2007 4192174.4831 2009 4169609.7865 1992 4153043.0281 2010 4119326.7865 2012 4088395.9382 2015 4088259.7865 2011 4083632.7865 2013 4081943.7865 2017 4077815.0899 2014 4061760.7865 2016 4046697.9382 2018 4008793.0000 2019 3962124.0000 1991 3912932.1798 1990 3796059.3315 Name: Interpersonal_violence, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Interpersonal_violence', color = 'Interpersonal_violence')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['HIV/AIDS'].sort_values(ascending = False)
Year_of_Death 2005 19640789.2821 2004 19580286.9975 2003 19276811.8539 2006 18901231.4257 2002 18584875.8539 2007 17973530.2821 2001 17666266.8539 2008 16821261.2821 2000 16605315.2821 2009 15609728.4257 1999 15234105.4257 2010 14675332.4257 2011 13816550.4257 1998 13795319.4257 2012 12855800.9975 1997 12532070.2821 2013 12119709.4257 2014 11551836.4257 1996 11425928.7104 2015 11112960.4257 2016 10741918.9975 2017 10198173.5693 1995 10167700.7104 2018 9337500.0000 2019 9028405.0000 1994 8701827.2821 1993 6841321.0000 1992 6185701.5668 1991 5023239.1386 1990 4007789.7104 Name: HIV/AIDS, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'HIV/AIDS', color = 'HIV/AIDS')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Drug_use_disorders'].sort_values(ascending = False)
Year_of_Death 2019 1364993.0000 2018 1312393.0000 2017 1283178.7536 2016 1227277.7125 2015 1148309.6714 2014 1080418.6714 2013 1027974.6714 2012 984279.7125 2011 969041.6714 2000 962053.5893 2010 954358.6714 2008 948614.5893 2009 942653.6714 2001 936115.6303 2007 935998.5893 1999 930171.6714 2005 923184.5893 2006 922428.6714 2002 911939.6303 1998 905271.6714 2003 888749.6303 1997 885115.5893 2004 879102.7125 1996 877975.5482 1995 859458.5482 1994 822243.5893 1993 734948.0000 1992 731173.4660 1991 677093.5071 1990 614522.5482 Name: Drug_use_disorders, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Drug_use_disorders', color = 'Drug_use_disorders')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Tuberculosis'].sort_values(ascending = False)
Year_of_Death 1992 18403612.5121 1991 18134219.2419 1990 17973351.9718 1994 17692251.7016 1995 17602585.9718 1997 17447276.7016 1996 17434983.9718 2000 17363037.7016 1993 17338616.0000 1998 17330402.1613 1999 17288292.1613 2001 16994197.4315 2002 16676834.4315 2003 16315248.4315 2004 15675802.8911 2005 15590202.7016 2006 15153465.1613 2007 14975454.7016 2008 14751457.7016 2009 14224110.1613 2010 13825329.1613 2011 13495323.1613 2012 13309616.8911 2013 13265546.1613 2014 13142615.1613 2015 12872771.1613 2016 12638673.8911 2017 12405768.6210 2018 11805677.0000 2019 11553475.0000 Name: Tuberculosis, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Tuberculosis', color = 'Tuberculosis')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Road_injuries'].sort_values(ascending = False)
Year_of_Death 2008 13039470.1423 2007 12945963.1423 2005 12927251.1423 2009 12910214.9139 2010 12870145.9139 2006 12812690.9139 2011 12748924.9139 2004 12694464.2996 2003 12691054.5281 2002 12588155.5281 2012 12574828.2996 2001 12468542.5281 2000 12402977.1423 2013 12384180.9139 2014 12201365.9139 1999 12147429.9139 2015 12074548.9139 1997 11987848.1423 1998 11978722.9139 1995 11965636.7566 1996 11956809.7566 2016 11951278.2996 2017 11854728.6854 1994 11838415.1423 1992 11718224.9850 2019 11682433.0000 2018 11665769.0000 1991 11599405.3708 1990 11517636.7566 1993 11241016.0000 Name: Road_injuries, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Road_injuries', color = 'Road_injuries')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Maternal_disorders'].sort_values(ascending = False)
Year_of_Death 1990 3023030.0306 1992 3010784.7634 1991 2993140.3970 1994 2919339.6642 1995 2901961.0306 1997 2886994.6642 1996 2875882.0306 2000 2870677.6642 1998 2869753.9313 1999 2868103.9313 1993 2830872.0000 2001 2817243.2978 2002 2756833.2978 2003 2690640.2978 2005 2653197.6642 2004 2633311.5649 2006 2585375.9313 2007 2537652.6642 2008 2491210.6642 2009 2435245.9313 2010 2392537.9313 2011 2342250.9313 2012 2258980.5649 2013 2230906.9313 2014 2145690.9313 2015 2081779.9313 2016 2027219.5649 2017 1973958.1985 2018 1908483.0000 2019 1892483.0000 Name: Maternal_disorders, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Maternal_disorders', color = 'Maternal_disorders')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Lower_respiratory_infections'].sort_values(ascending = False)
Year_of_Death 1990 33932816.7965 1991 33715996.8689 1992 33606797.9413 1994 32650901.7241 1995 32414420.7965 1993 31974430.0000 1996 31879885.7965 1997 31379054.7241 1998 30723395.5793 1999 30260562.5793 2000 29925864.7241 2001 29157664.6517 2002 28756224.6517 2003 28345485.6517 2005 27771849.7241 2004 27642279.5069 2006 27257734.5793 2007 27188599.7241 2008 27048864.7241 2009 26524081.5793 2010 26276707.5793 2013 26242641.5793 2015 26235854.5793 2014 26204173.5793 2011 26115335.5793 2012 26017300.5069 2016 25971009.5069 2017 25591427.4345 2019 25041269.0000 2018 24974134.0000 Name: Lower_respiratory_infections, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Lower_respiratory_infections', color = 'Lower_respiratory_infections')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Neonatal_disorders'].sort_values(ascending = False)
Year_of_Death 1990 29949352.3528 1991 29761812.9303 1992 29617007.5079 1994 28829370.7753 1995 28706087.3528 1996 28400568.3528 1993 28164872.0000 1997 28090018.7753 1998 27687828.6202 1999 27436908.6202 2000 27369937.7753 2001 27001871.1978 2002 26827207.1978 2003 26636230.1978 2005 26254143.7753 2004 26231067.0427 2006 25748573.6202 2007 25576518.7753 2008 25257848.7753 2009 24681615.6202 2010 24238073.6202 2011 23806620.6202 2012 23301105.0427 2013 23008173.6202 2014 22413443.6202 2015 21898408.6202 2016 20994612.0427 2017 20083722.4652 2018 18833230.0000 2019 18245288.0000 Name: Neonatal_disorders, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Neonatal_disorders', color = 'Neonatal_disorders')
fig.show()
new_df.groupby(['Year_of_Death']).sum()[ 'Alcohol_use_disorders'].sort_values(ascending = False)
Year_of_Death 2005 1900320.6117 2004 1869142.1282 2003 1865017.4506 2006 1849987.2894 2002 1828438.4506 2007 1825957.6117 2008 1803677.6117 2001 1782497.4506 2009 1744117.2894 2000 1740664.6117 2010 1721060.2894 2019 1709565.0000 2016 1696197.1282 2017 1695005.9670 2011 1692581.2894 2015 1688641.2894 2018 1685875.0000 2014 1674131.2894 1999 1673449.2894 2013 1668957.2894 2012 1667714.1282 1995 1651730.7729 1996 1648098.7729 1997 1633218.6117 1998 1630028.2894 1994 1617404.6117 1993 1456631.0000 1992 1414127.0953 1991 1316473.9341 1990 1249542.7729 Name: Alcohol_use_disorders, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Alcohol_use_disorders', color = 'Alcohol_use_disorders')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Exposure_to_forces_of_nature'].sort_values(ascending = False)
Year_of_Death 2004 2445688.9285 2008 2228889.1835 2010 1987123.3468 1991 1436020.0202 2005 828567.1835 1999 642996.3468 1998 438573.3468 1990 437229.6019 2011 342918.3468 2003 311628.7652 2001 302833.7652 2013 265597.3468 1995 208433.6019 2006 204778.3468 1993 203190.0000 1997 181496.1835 2007 167128.1835 1996 162359.6019 2015 139888.3468 1994 137975.1835 1992 136285.4386 2017 129267.5101 2009 118694.3468 2018 109282.0000 2000 103354.1835 2002 98126.7652 2012 95662.9285 2016 94319.9285 2014 83803.3468 2019 59173.0000 Name: Exposure_to_forces_of_nature, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Exposure_to_forces_of_nature', color = 'Exposure_to_forces_of_nature')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Diarrheal_diseases']
Year_of_Death 1990 29251537.1969 1991 29439506.0330 1992 29218873.8690 1993 27432388.0000 1994 27684815.3608 1995 27111815.1969 1996 26564254.1969 1997 26145626.3608 1998 25658341.6886 1999 25205339.6886 2000 24827374.3608 2001 24156640.5247 2002 23520204.5247 2003 22845752.5247 2004 21943699.8526 2005 21794590.3608 2006 21491562.6886 2007 21342752.3608 2008 21030841.3608 2009 20202987.6886 2010 19737087.6886 2011 19469816.6886 2012 18727441.8526 2013 18348864.6886 2014 17729036.6886 2015 17266065.6886 2016 16795571.8526 2017 16636431.0165 2018 15616728.0000 2019 15181793.0000 Name: Diarrheal_diseases, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Diarrheal_diseases', color = 'Diarrheal_diseases')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Environmental_heat_and_cold_exposure'].sort_values(ascending = False)
Year_of_Death 1995 750927.6152 1994 745401.5593 2003 742497.5034 2002 719415.5034 2005 706402.5593 1996 700090.6152 2001 687579.5034 2004 687161.3915 2000 677036.5593 1993 668640.0000 1998 667525.4474 2006 665131.4474 1997 660825.5593 1999 652485.4474 1992 628736.7271 2007 620949.5593 2010 606374.4474 2008 600800.5593 1991 593810.6712 1990 576158.6152 2009 567654.4474 2011 535962.4474 2015 535081.4474 2012 531958.3915 2013 515092.4474 2014 506762.4474 2016 492241.3915 2019 479449.0000 2017 477866.3356 2018 464640.0000 Name: Environmental_heat_and_cold_exposure, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Environmental_heat_and_cold_exposure', color = 'Environmental_heat_and_cold_exposure')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Nutritional_deficiencies'].sort_values(ascending = False)
Year_of_Death 1990 7482044.6303 1991 7237207.6876 1995 7098006.6303 1992 6978513.7449 1996 6619370.6303 1993 6507485.0000 1994 6437941.5730 1997 6368868.5730 1998 6071071.4584 1999 5807843.4584 2000 5599694.5730 2001 5333006.5157 2002 5094431.5157 2003 4173176.5157 2004 3916311.4011 2005 3799403.5730 2006 3632662.4584 2007 3523016.5730 2008 3409643.5730 2009 3255591.4584 2010 3246251.4584 2011 3173359.4584 2012 3010665.4011 2013 2964201.4584 2014 2897287.4584 2015 2843148.4584 2016 2771400.4011 2017 2697389.3438 2018 2508095.0000 2019 2444227.0000 Name: Nutritional_deficiencies, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Nutritional_deficiencies', color = 'Nutritional_deficiencies')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Self-harm'].sort_values(ascending = False)
Year_of_Death 2000 8821783.2447 1999 8781565.1958 1995 8726805.7692 2005 8683788.2447 1998 8674909.1958 1996 8664226.7692 1997 8661926.2447 1994 8618473.2447 2001 8604733.7202 2003 8542524.7202 2004 8525594.6713 2002 8508446.7202 2006 8503527.1958 2007 8447368.2447 2008 8399150.2447 1992 8245210.8181 2009 8223743.1958 2010 8207513.1958 2011 8132530.1958 1993 8064378.0000 1991 8042053.2936 2012 7997491.6713 2013 7909681.1958 1990 7875843.7692 2014 7798169.1958 2015 7771952.1958 2016 7740495.6713 2017 7714167.1468 2019 7682558.0000 2018 7648006.0000 Name: Self-harm, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Self-harm', color = 'Self-harm')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Conflict_and_terrorism'].sort_values(ascending = False)
Year_of_Death 1994 6143653.5443 2014 1626829.2355 2015 1449403.2355 2016 1370595.5810 1999 1334037.2355 2000 1135915.5443 2017 1118331.9266 1990 1110766.1988 1998 1030349.2355 2013 1023420.2355 2012 963698.5810 1997 953352.5443 2018 876568.0000 1996 847653.1988 1991 838714.8532 2009 682413.2355 1995 676303.1988 2003 662922.8899 2006 657245.2355 2001 653464.8899 2004 645512.5810 2007 642089.5443 1992 628510.5076 2002 627076.8899 2008 613769.5443 2011 572363.2355 1993 572100.0000 2005 549036.5443 2019 529821.0000 2010 529285.2355 Name: Conflict_and_terrorism, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Conflict_and_terrorism', color = 'Conflict_and_terrorism')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Diabetes_mellitus'].sort_values(ascending = False)
Year_of_Death 2019 15409833.0000 2018 14939751.0000 2017 14717712.3670 2016 14348660.5949 2015 13936608.8227 2014 13488725.8227 2013 13072960.8227 2012 12641478.5949 2011 12305027.8227 2010 11993099.8227 2009 11772535.8227 2008 11662758.2784 2007 11376903.2784 2006 11088470.8227 2005 10974566.2784 2004 10534450.5949 2003 10448688.0506 2002 10153706.0506 2001 9798067.0506 2000 9550049.2784 1999 9208636.8227 1998 8957463.8227 1997 8770191.2784 1996 8518085.5062 1995 8263912.5062 1994 8005891.2784 1992 7643305.9619 1993 7384169.0000 1991 7382669.7341 1990 7158244.5062 Name: Diabetes_mellitus, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Diabetes_mellitus', color = 'Diabetes_mellitus')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Poisonings'].sort_values(ascending = False)
Year_of_Death 2005 936083.1111 1994 923220.1111 1995 920443.2222 2004 920138.7778 2003 918305.0000 2002 910720.0000 1992 909005.4444 2006 908342.8889 1996 905442.2222 2001 904217.0000 2008 903574.1111 2000 902435.1111 2007 901256.1111 1991 900314.3333 1990 898864.2222 2009 889897.8889 2010 887901.8889 1997 887887.1111 1993 882970.0000 1999 881250.8889 1998 874906.8889 2011 872625.8889 2012 854664.7778 2013 848172.8889 2014 832759.8889 2015 822652.8889 2016 809336.7778 2017 791345.6667 2018 765508.0000 2019 758680.0000 Name: Poisonings, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Poisonings', color = 'Poisonings')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Protein-energy_malnutrition'].sort_values(ascending = False)
Year_of_Death 1990 6471534.2297 1995 6271585.2297 1991 6249856.6142 1992 6022943.9988 1996 5830991.2297 1993 5622720.0000 1997 5610297.8452 1994 5576329.8452 1998 5359881.0762 1999 5145720.0762 2000 4967192.8452 2001 4733175.4607 2002 4532484.4607 2003 3651981.4607 2004 3430215.6916 2005 3321914.8452 2006 3165333.0762 2007 3060478.8452 2008 2951051.8452 2009 2814886.0762 2010 2810585.0762 2011 2739772.0762 2012 2581215.6916 2013 2538171.0762 2014 2475561.0762 2015 2426802.0762 2016 2361481.6916 2017 2292121.3071 2018 2120805.0000 2019 2062098.0000 Name: Protein-energy_malnutrition, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Protein-energy_malnutrition', color = 'Protein-energy_malnutrition')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Terrorism'].sort_values(ascending = False)
Year_of_Death 2014 190046.2165 2015 173484.4524 2016 158046.0934 2017 134514.2729 2013 125141.3961 2012 108305.7551 2007 103429.8782 2006 94844.0578 2019 93245.9865 2018 93245.9865 1993 93245.9865 2009 90332.2269 2011 89695.8782 2004 89520.8322 2010 89137.3501 2005 87792.4732 2008 87539.5756 1990 84655.6987 1997 83411.2062 2001 81160.1602 2002 79722.1141 1998 79720.3501 1991 76624.6780 2003 75258.1141 1992 73229.7240 2000 71182.1602 1999 70596.8115 1996 70489.4984 1994 67772.1958 1995 67202.0266 Name: Terrorism, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Terrorism', color = 'Terrorism')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Cardiovascular_diseases'].sort_values(ascending = False)
Year_of_Death 2019 186591929.0000 2018 182185452.0000 2017 181262400.4337 2016 178986724.1727 2015 176529080.9116 2014 172833776.9116 2013 170640947.9116 2012 167696295.1727 2011 166208710.9116 2010 164238697.9116 2009 161477181.9116 2008 161211489.3895 2007 158482656.3895 2005 157518557.3895 2006 156208079.9116 2003 154531129.6506 2004 153470631.1727 2002 152591536.6506 2001 149569869.6506 2000 148011255.3895 1999 144719830.9116 1997 142261492.3895 1998 142255338.9116 1996 141650682.1285 1995 140908200.1285 1994 139395446.3895 1992 135487138.6064 1991 132898527.8674 1993 131636930.0000 1990 130850467.1285 Name: Cardiovascular_diseases, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Malaria', color = 'Cardiovascular_diseases')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Chronic_kidney_disease'].sort_values(ascending = False)
Year_of_Death 2019 14260228.0000 2018 13878564.0000 2017 13736980.7176 2016 13518940.1705 2015 13183089.6235 2014 12785215.6235 2013 12455570.6235 2012 12041648.1705 2011 11743365.6235 2010 11382887.6235 2009 11046540.6235 2008 10839064.5293 2007 10484535.5293 2006 10123539.6235 2005 9941601.5293 2004 9495341.1705 2003 9364362.0764 2002 9083340.0764 2001 8769196.0764 2000 8524580.5293 1999 8154976.6235 1998 7885471.6235 1997 7679627.5293 1996 7448281.9823 1995 7239234.9823 1994 7019020.5293 1992 6778985.8881 1991 6581273.4352 1993 6477967.0000 1990 6421136.9823 Name: Chronic_kidney_disease, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Chronic_kidney_disease', color = 'Chronic_kidney_disease')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Chronic_Respiratory_diseases'].sort_values(ascending = False)
Year_of_Death 2019 40016895.0000 2018 39151687.0000 2017 39024487.4951 2016 38610803.7443 2015 38250571.9935 2014 37843083.9935 2013 37439201.9935 2012 36778059.7443 2011 36703234.9935 2008 36662561.4919 2010 36398710.9935 2003 36344655.2427 2005 36341575.4919 2002 36324463.2427 2009 36278019.9935 2007 36228137.4919 2001 36073391.2427 2000 36047488.4919 2006 35898880.9935 2004 35778628.7443 1999 35486341.9935 1997 35352664.4919 1998 35294173.9935 1996 35080883.7411 1995 34782877.7411 1994 34487236.4919 1992 33996163.2395 1991 33276176.9903 1993 32872069.0000 1990 32588183.7411 Name: Chronic_Respiratory_diseases, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Chronic_Respiratory_diseases', color = 'Chronic_Respiratory_diseases')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Cirrhosis_liver_diseases'].sort_values(ascending = False)
Year_of_Death 2019 14591030.0000 2017 14432221.0112 2018 14362805.0000 2016 14268548.3464 2015 14132792.6816 2014 13926965.6816 2013 13892259.6816 2012 13805015.3464 2011 13804194.6816 2008 13732511.3521 2010 13729498.6816 2009 13633165.6816 2007 13552004.3521 2005 13380611.3521 2006 13346319.6816 2004 12970987.3464 2003 12928752.0169 2002 12692165.0169 2001 12423727.0169 2000 12249036.3521 1999 11971720.6816 1998 11804089.6816 1997 11773243.3521 1996 11681764.6873 1995 11576661.6873 1994 11382063.3521 1992 11066883.3577 1991 10862762.0225 1993 10703659.0000 1990 10671551.6873 Name: Cirrhosis_liver_diseases, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Cirrhosis_liver_diseases', color = 'Cirrhosis_liver_diseases')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Digestive_diseases'].sort_values(ascending = False)
Year_of_Death 2019 25696297.0000 2017 25411151.4577 2018 25288011.0000 2016 25117581.3673 2015 24857941.2769 2014 24459532.2769 2013 24316539.2769 2012 24044622.3673 2011 23998490.2769 2010 23819227.2769 2008 23811656.0961 2009 23638516.2769 2007 23527698.0961 2005 23276918.0961 2006 23202617.2769 2003 22684489.1865 2004 22647249.3673 2002 22386264.1865 2001 22021787.1865 2000 21821619.0961 1999 21405667.2769 1998 21164811.2769 1997 21164543.0961 1996 21047852.0057 1995 20934902.0057 1994 20684270.0961 1992 20297518.8250 1991 19956234.9154 1990 19640078.0057 1993 19579378.0000 Name: Digestive_diseases, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Digestive_diseases', color = 'Digestive_diseases')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Acute_hepatitis'].sort_values(ascending = False)
Year_of_Death 1990 1661965.4925 1991 1657379.7191 1992 1646588.9457 1994 1591217.2659 1995 1574135.4925 1993 1569312.0000 1996 1537720.4925 1997 1519494.2659 1998 1488657.8127 1999 1464685.8127 2000 1446091.2659 2001 1403053.0393 2002 1352192.0393 2003 1305056.0393 2005 1291444.2659 2004 1274594.5861 2006 1248981.8127 2007 1217801.2659 2008 1186935.2659 2009 1132842.8127 2010 1103008.8127 2011 1073791.8127 2012 1050202.5861 2013 998151.8127 2014 937672.8127 2015 896927.8127 2016 852222.5861 2017 825085.3596 2018 782313.0000 2019 765188.0000 Name: Acute_hepatitis, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Acute_hepatitis', color = 'Acute_hepatitis')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Alzheimer disease'].sort_values(ascending = False)
Year_of_Death 2019 16988702.0000 2018 16437635.0000 2017 16067495.6794 2016 15515803.6260 2015 14972793.5725 2014 14441797.5725 2013 13938472.5725 2012 13414069.6260 2011 12971799.5725 2010 12499823.5725 2009 12014697.5725 2008 11637073.4657 2007 11188383.4657 2006 10696557.5725 2005 10393292.4657 2004 9894720.6260 2003 9641247.5191 2002 9314888.5191 2001 8989577.5191 2000 8737762.4657 1999 8399477.5725 1998 8161537.5725 1997 8017137.4657 1996 7840580.4122 1995 7610644.4122 1994 7330315.4657 1992 6955305.3054 1993 6697687.0000 1991 6673846.3588 1990 6393869.4122 Name: Alzheimer disease, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Alzheimer disease', color = 'Alzheimer disease')
fig.show()
new_df.groupby(['Year_of_Death']).sum()['Parkinson disease'].sort_values(ascending = False)
Year_of_Death 2019 3770569.0000 2018 3654087.0000 2017 3586867.1019 2016 3509369.1189 2015 3429082.1358 2014 3336769.1358 2013 3240934.1358 2012 3126003.1189 2011 3045024.1358 2010 2958818.1358 2009 2872161.1358 2008 2815241.1698 2007 2712474.1698 2006 2612923.1358 2005 2566524.1698 2004 2447128.1189 2003 2410932.1528 2002 2334243.1528 2001 2243045.1528 2000 2172943.1698 1999 2082333.1358 1998 2015003.1358 1997 1973769.1698 1996 1925851.1868 1995 1874415.1868 1994 1817816.1698 1992 1748998.2207 1991 1698855.2037 1993 1682132.0000 1990 1651046.1868 Name: Parkinson disease, dtype: float64
fig = px.bar(new_df, x = 'Year_of_Death', y = 'Parkinson disease', color = 'Parkinson disease')
fig.show()
new_df.columns
Index(['Country', 'Year_of_Death', 'Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'],
dtype='object')
new_df.groupby(['Country']).sum()['Meningitis'].sort_values(ascending = False)[:20]
Country World 10530812.0000 World Bank Lower Middle Income 6015299.0000 Commonwealth 5676380.0000 African Union 5559509.0000 Africa 5559509.0000 Sub-Saharan Africa - World Bank region 5466427.0000 African Region 5325372.0000 Low SDI 4907585.0000 Commonwealth Middle Income 4749263.0000 Asia 4380260.0000 G20 3334483.0000 Low-middle SDI 3315974.0000 World Bank Low Income 3262367.0000 Western sub-Saharan Africa 3036082.0000 South Asia - World Bank region 2985858.0000 South-East Asia Region 2684670.0000 India 2008944.0000 Eastern sub-Saharan Africa 1787598.0000 Middle SDI 1667936.0000 Nigeria 1520376.0000 Name: Meningitis, dtype: float64
fig = px.bar(new_df, x = 'Country', y = 'Meningitis', color = 'Meningitis')
fig.show()
array(['Afghanistan', 'Africa', 'African Region', 'African Union',
'Albania', 'Algeria', 'America', 'American Samoa',
'Andean Latin America', 'Andorra', 'Angola', 'Antigua and Barbuda',
'Argentina', 'Armenia', 'Asia', 'Australasia',
'Australasia & Oceania', 'Australia', 'Austria', 'Azerbaijan',
'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus',
'Belgium', 'Belize', 'Benin', 'Bermuda', 'Bhutan', 'Bolivia',
'Bosnia and Herzegovina', 'Bosnia-Herzegovina', 'Botswana',
'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi',
'Cambodia', 'Cameroon', 'Canada', 'Cape Verde', 'Caribbean',
'Central African Republic', 'Central America & Caribbean',
'Central Asia', 'Central Europe',
'Central Europe, Eastern Europe, and Central Asia',
'Central Latin America', 'Central sub-Saharan Africa', 'Chad',
'Chile', 'China', 'Colombia', 'Commonwealth',
'Commonwealth High Income', 'Commonwealth Low Income',
'Commonwealth Middle Income', 'Comoros', 'Congo', 'Cook Islands',
'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba', 'Cyprus',
'Czechia', 'Czechoslovakia', 'Democratic Republic of Congo',
'Denmark', 'Djibouti', 'Dominica', 'Dominican Republic',
'East Asia', 'East Asia & Pacific - World Bank region',
'East Germany (GDR)', 'East Timor', 'Eastern Europe',
'Eastern Mediterranean Region', 'Eastern sub-Saharan Africa',
'Ecuador', 'Egypt', 'El Salvador', 'England', 'Equatorial Guinea',
'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'Europe',
'Europe & Central Asia - World Bank region', 'European Region',
'European Union', 'Fiji', 'Finland', 'France', 'French Guiana',
'French Polynesia', 'G20', 'Gabon', 'Gambia', 'Georgia', 'Germany',
'Ghana', 'Greece', 'Greenland', 'Grenada', 'Guadeloupe', 'Guam',
'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',
'High SDI', 'High-income', 'High-income Asia Pacific',
'High-income North America', 'High-middle SDI', 'Honduras',
'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia',
'International', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',
'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',
'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos',
'Latin America & Caribbean - World Bank region', 'Latvia',
'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Lithuania', 'Low SDI',
'Low-middle SDI', 'Luxembourg', 'Macau', 'Madagascar', 'Malawi',
'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands',
'Martinique', 'Mauritania', 'Mauritius', 'Mexico',
'Micronesia (country)', 'Middle East & North Africa', 'Middle SDI',
'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco',
'Mozambique', 'Myanmar', 'Namibia', 'Nauru', 'Nepal',
'Netherlands', 'New Caledonia', 'New Zealand', 'Nicaragua',
'Niger', 'Nigeria', 'Niue', 'Nordic Region',
'North Africa and Middle East', 'North America', 'North Korea',
'North Macedonia', 'Northern Ireland', 'Northern Mariana Islands',
'Norway', 'OECD Countries', 'Oceania', 'Oman', 'Pakistan', 'Palau',
'Palestine', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru',
'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',
'Region of the Americas', 'Romania', 'Russia', 'Rwanda',
'Saint Kitts and Nevis', 'Saint Lucia',
'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',
'Sao Tome and Principe', 'Saudi Arabia', 'Scotland', 'Senegal',
'Serbia', 'Serbia-Montenegro', 'Seychelles', 'Sierra Leone',
'Singapore', 'Slovakia', 'Slovenia', 'Solomon Islands', 'Somalia',
'South Africa', 'South America', 'South Asia',
'South Asia - World Bank region', 'South Korea', 'South Sudan',
'South-East Asia Region', 'Southeast Asia',
'Southeast Asia, East Asia, and Oceania', 'Southern Latin America',
'Southern sub-Saharan Africa', 'Spain', 'Sri Lanka',
'Sub-Saharan Africa', 'Sub-Saharan Africa - World Bank region',
'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',
'Tajikistan', 'Tanzania', 'Thailand', 'Timor', 'Togo', 'Tokelau',
'Tonga', 'Trinidad and Tobago', 'Tropical Latin America',
'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'USSR', 'Uganda',
'Ukraine', 'United Arab Emirates', 'United Kingdom',
'United States', 'United States Virgin Islands', 'Uruguay',
'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam', 'Wales',
'Wallis and Futuna', 'West Germany (FRG)', 'Western Europe',
'Western Pacific Region', 'Western Sahara',
'Western sub-Saharan Africa', 'World', 'World (excluding China)',
'World Bank High Income', 'World Bank Low Income',
'World Bank Lower Middle Income', 'World Bank Upper Middle Income',
'Yemen', 'Yugoslavia', 'Zaire', 'Zambia', 'Zimbabwe'], dtype=object)
new_df.columns
Index(['Country', 'Year_of_Death', 'Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'],
dtype='object')
new_df.pivot_table(index = ['Country', 'Year_of_Death'], values = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'])
| Acute_hepatitis | Alcohol_use_disorders | Alzheimer disease | Cardiovascular_diseases | Chronic_Respiratory_diseases | Chronic_kidney_disease | Cirrhosis_liver_diseases | Conflict_and_terrorism | Diabetes_mellitus | Diarrheal_diseases | Digestive_diseases | Drowning | Drug_use_disorders | Environmental_heat_and_cold_exposure | Exposure_to_forces_of_nature | Fire_heat_and_hot_substances | HIV/AIDS | Interpersonal_violence | Lower_respiratory_infections | Malaria | Maternal_disorders | Meningitis | Neonatal_disorders | Neoplasms | Nutritional_deficiencies | Parkinson disease | Poisonings | Protein-energy_malnutrition | Road_injuries | Self-harm | Terrorism | Tuberculosis | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | Year_of_Death | ||||||||||||||||||||||||||||||||
| Afghanistan | 1990 | 2985.0000 | 72.0000 | 1116.0000 | 44899.0000 | 5945.0000 | 3709.0000 | 2673.0000 | 1490.0000 | 2108.0000 | 4235.0000 | 5005.0000 | 1370.0000 | 93.0000 | 175.0000 | 0.0000 | 323.0000 | 34.0000 | 1538.0000 | 23741.0000 | 93.0000 | 2655.0000 | 2159.0000 | 15612.0000 | 11580.0000 | 2087.0000 | 371.0000 | 338.0000 | 2054.0000 | 4154.0000 | 696.0000 | 12.0000 | 4661.0000 |
| 1991 | 3092.0000 | 75.0000 | 1136.0000 | 45492.0000 | 6050.0000 | 3724.0000 | 2728.0000 | 3370.0000 | 2120.0000 | 4927.0000 | 5120.0000 | 1391.0000 | 102.0000 | 113.0000 | 1347.0000 | 332.0000 | 41.0000 | 2001.0000 | 24504.0000 | 189.0000 | 2885.0000 | 2218.0000 | 17128.0000 | 11796.0000 | 2153.0000 | 374.0000 | 351.0000 | 2119.0000 | 4472.0000 | 751.0000 | 68.0000 | 4743.0000 | |
| 1992 | 3325.0000 | 80.0000 | 1162.0000 | 46557.0000 | 6223.0000 | 3776.0000 | 2830.0000 | 4344.0000 | 2153.0000 | 6123.0000 | 5335.0000 | 1514.0000 | 118.0000 | 38.0000 | 614.0000 | 360.0000 | 48.0000 | 2299.0000 | 27404.0000 | 239.0000 | 3315.0000 | 2475.0000 | 20060.0000 | 12218.0000 | 2441.0000 | 378.0000 | 386.0000 | 2404.0000 | 5106.0000 | 855.0000 | 49.0000 | 4976.0000 | |
| 1993 | 3601.0000 | 85.0000 | 1187.0000 | 47951.0000 | 6445.0000 | 3862.0000 | 2943.0000 | 4096.0000 | 2195.0000 | 8174.0000 | 5568.0000 | 1687.0000 | 132.0000 | 41.0000 | 225.0000 | 396.0000 | 56.0000 | 2589.0000 | 31116.0000 | 108.0000 | 3671.0000 | 2812.0000 | 22335.0000 | 12634.0000 | 2837.0000 | 384.0000 | 425.0000 | 2797.0000 | 5681.0000 | 943.0000 | 349.2359 | 5254.0000 | |
| 1994 | 3816.0000 | 88.0000 | 1211.0000 | 49308.0000 | 6664.0000 | 3932.0000 | 3027.0000 | 8959.0000 | 2231.0000 | 8215.0000 | 5739.0000 | 1809.0000 | 142.0000 | 44.0000 | 160.0000 | 420.0000 | 63.0000 | 2849.0000 | 33390.0000 | 211.0000 | 3863.0000 | 3027.0000 | 23288.0000 | 12914.0000 | 3081.0000 | 391.0000 | 451.0000 | 3038.0000 | 6001.0000 | 993.0000 | 22.0000 | 5470.0000 | |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Zimbabwe | 2015 | 146.0000 | 48.0000 | 754.0000 | 16649.0000 | 2751.0000 | 2108.0000 | 1956.0000 | 13.0000 | 3176.0000 | 5102.0000 | 4202.0000 | 770.0000 | 104.0000 | 37.0000 | 15.0000 | 632.0000 | 29162.0000 | 1302.0000 | 12974.0000 | 2518.0000 | 1355.0000 | 1439.0000 | 9278.0000 | 11161.0000 | 3019.0000 | 215.0000 | 381.0000 | 2990.0000 | 2373.0000 | 2235.0000 | 349.2359 | 11214.0000 |
| 2016 | 146.0000 | 49.0000 | 767.0000 | 16937.0000 | 2788.0000 | 2160.0000 | 1962.0000 | 6.0000 | 3259.0000 | 5002.0000 | 4264.0000 | 801.0000 | 110.0000 | 37.0000 | 31.0000 | 648.0000 | 27141.0000 | 1342.0000 | 13024.0000 | 2050.0000 | 1338.0000 | 1457.0000 | 9065.0000 | 11465.0000 | 3056.0000 | 219.0000 | 393.0000 | 3027.0000 | 2436.0000 | 2297.0000 | 349.2359 | 10998.0000 | |
| 2017 | 144.0000 | 50.0000 | 781.0000 | 17187.0000 | 2818.0000 | 2196.0000 | 2007.0000 | 5.0000 | 3313.0000 | 4948.0000 | 4342.0000 | 818.0000 | 115.0000 | 37.0000 | 251.0000 | 654.0000 | 24846.0000 | 1363.0000 | 12961.0000 | 2116.0000 | 1312.0000 | 1460.0000 | 8901.0000 | 11744.0000 | 2990.0000 | 223.0000 | 398.0000 | 2962.0000 | 2473.0000 | 2338.0000 | 0.0000 | 10762.0000 | |
| 2018 | 139.0000 | 51.0000 | 795.0000 | 17460.0000 | 2849.0000 | 2240.0000 | 2030.0000 | 9.0000 | 3381.0000 | 4745.0000 | 4377.0000 | 825.0000 | 121.0000 | 37.0000 | 0.0000 | 657.0000 | 22106.0000 | 1396.0000 | 12860.0000 | 2088.0000 | 1294.0000 | 1450.0000 | 8697.0000 | 12038.0000 | 2918.0000 | 227.0000 | 400.0000 | 2890.0000 | 2509.0000 | 2372.0000 | 349.2359 | 10545.0000 | |
| 2019 | 136.0000 | 53.0000 | 812.0000 | 17810.0000 | 2891.0000 | 2292.0000 | 2065.0000 | 11.0000 | 3460.0000 | 4635.0000 | 4437.0000 | 827.0000 | 127.0000 | 37.0000 | 660.0000 | 662.0000 | 20722.0000 | 1434.0000 | 12897.0000 | 2068.0000 | 1294.0000 | 1450.0000 | 8609.0000 | 12353.0000 | 2884.0000 | 232.0000 | 405.0000 | 2855.0000 | 2554.0000 | 2403.0000 | 349.2359 | 10465.0000 |
8254 rows × 32 columns
new_df.pivot_table(index = ['Country'], values = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease']).sum().sort_values(ascending = False)
Cardiovascular_diseases 166212377.5127 Neoplasms 87430763.2434 Chronic_Respiratory_diseases 38529866.0122 Lower_respiratory_infections 30557577.2160 Neonatal_disorders 27157753.2157 Digestive_diseases 24206168.5166 Diarrheal_diseases 23868024.9714 Tuberculosis 16424194.1576 HIV/AIDS 13844668.4669 Cirrhosis_liver_diseases 13679096.2154 Road_injuries 13043367.9700 Diabetes_mellitus 11554814.7572 Alzheimer disease 11495546.3441 Chronic_kidney_disease 10590617.7096 Malaria 9320929.0588 Self-harm 8811249.6695 Nutritional_deficiencies 4859714.7899 Interpersonal_violence 4487543.5562 Protein-energy_malnutrition 4231325.6642 Meningitis 3782542.4292 Drowning 3672062.9201 Parkinson disease 2744535.9748 Maternal_disorders 2729988.3602 Alcohol_use_disorders 1789105.2238 Acute_hepatitis 1343764.3914 Fire_heat_and_hot_substances 1302337.5567 Conflict_and_terrorism 1102750.7486 Drug_use_disorders 1016697.9654 Poisonings 934409.5556 Environmental_heat_and_cold_exposure 655457.3875 Exposure_to_forces_of_nature 500566.5772 Terrorism 97073.9132 dtype: float64
new_df.pivot_table(index = ['Country'], values = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease']).mean().sort_values(ascending = False)
Cardiovascular_diseases 567277.7390 Neoplasms 298398.5094 Chronic_Respiratory_diseases 131501.2492 Lower_respiratory_infections 104292.0724 Neonatal_disorders 92688.5775 Digestive_diseases 82614.9096 Diarrheal_diseases 81460.8361 Tuberculosis 56055.2702 HIV/AIDS 47251.4282 Cirrhosis_liver_diseases 46686.3352 Road_injuries 44516.6142 Diabetes_mellitus 39436.2278 Alzheimer disease 39233.9466 Chronic_kidney_disease 36145.4529 Malaria 31812.0446 Self-harm 30072.5245 Nutritional_deficiencies 16586.0573 Interpersonal_violence 15315.8483 Protein-energy_malnutrition 14441.3845 Meningitis 12909.7011 Drowning 12532.6380 Parkinson disease 9367.0170 Maternal_disorders 9317.3664 Alcohol_use_disorders 6106.1612 Acute_hepatitis 4586.2266 Fire_heat_and_hot_substances 4444.8381 Conflict_and_terrorism 3763.6544 Drug_use_disorders 3469.9589 Poisonings 3189.1111 Environmental_heat_and_cold_exposure 2237.0559 Exposure_to_forces_of_nature 1708.4184 Terrorism 331.3103 dtype: float64
new_df.pivot_table(index = 'Country', values = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'], aggfunc = 'sum')
| Acute_hepatitis | Alcohol_use_disorders | Alzheimer disease | Cardiovascular_diseases | Chronic_Respiratory_diseases | Chronic_kidney_disease | Cirrhosis_liver_diseases | Conflict_and_terrorism | Diabetes_mellitus | Diarrheal_diseases | Digestive_diseases | Drowning | Drug_use_disorders | Environmental_heat_and_cold_exposure | Exposure_to_forces_of_nature | Fire_heat_and_hot_substances | HIV/AIDS | Interpersonal_violence | Lower_respiratory_infections | Malaria | Maternal_disorders | Meningitis | Neonatal_disorders | Neoplasms | Nutritional_deficiencies | Parkinson disease | Poisonings | Protein-energy_malnutrition | Road_injuries | Self-harm | Terrorism | Tuberculosis | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | ||||||||||||||||||||||||||||||||
| Afghanistan | 98108.0000 | 3256.0000 | 41998.0000 | 1607042.0000 | 209857.0000 | 134676.0000 | 98419.0000 | 280520.0000 | 93207.0000 | 245832.0000 | 186959.0000 | 56535.0000 | 7094.0000 | 2187.0000 | 16770.0000 | 13559.0000 | 4282.0000 | 108228.0000 | 822179.0000 | 13924.0000 | 129621.0000 | 78665.0000 | 697534.0000 | 469611.0000 | 71453.0000 | 13397.0000 | 14531.0000 | 70163.0000 | 208332.0000 | 37054.0000 | 40240.7077 | 147637.0000 |
| Africa | 696194.0000 | 176794.0000 | 1639248.0000 | 36704118.0000 | 5707716.0000 | 3630423.0000 | 6347525.0000 | 1575617.0000 | 4240427.0000 | 26224276.0000 | 9900886.0000 | 945160.0000 | 75298.0000 | 165448.0000 | 34977.0000 | 803923.0000 | 29106962.0000 | 2178223.0000 | 25228868.0000 | 21686225.0000 | 2980827.0000 | 5559509.0000 | 25075847.0000 | 13633297.0000 | 5322341.0000 | 423800.0000 | 613852.0000 | 5145422.0000 | 6359218.0000 | 1893844.0000 | 10477.0771 | 13777914.0000 |
| African Region | 525500.0000 | 163833.0000 | 1252189.0000 | 24875081.0000 | 4564405.0000 | 2779058.0000 | 4578870.0000 | 1402850.0000 | 3602991.0000 | 24485553.0000 | 7763233.0000 | 801804.0000 | 53177.0000 | 152369.0000 | 27010.0000 | 659574.0000 | 28888674.0000 | 2056987.0000 | 23007935.0000 | 21462693.0000 | 2740727.0000 | 5325372.0000 | 22661167.0000 | 11094707.0000 | 4940860.0000 | 318566.0000 | 555599.0000 | 4778254.0000 | 4839529.0000 | 1651040.0000 | 10477.0771 | 13028578.0000 |
| African Union | 696194.0000 | 176794.0000 | 1639248.0000 | 36704118.0000 | 5707716.0000 | 3630423.0000 | 6347525.0000 | 1575617.0000 | 4240427.0000 | 26224276.0000 | 9900886.0000 | 945160.0000 | 75298.0000 | 165448.0000 | 34977.0000 | 803923.0000 | 29106962.0000 | 2178223.0000 | 25228868.0000 | 21686225.0000 | 2980827.0000 | 5559509.0000 | 25075847.0000 | 13633297.0000 | 5322341.0000 | 423800.0000 | 613852.0000 | 5145422.0000 | 6359218.0000 | 1893844.0000 | 10477.0771 | 13777914.0000 |
| Albania | 44.0000 | 458.0000 | 16551.0000 | 270603.0000 | 22632.0000 | 7637.0000 | 8717.0000 | 2145.0000 | 4054.0000 | 677.0000 | 14907.0000 | 2397.0000 | 634.0000 | 164.0000 | 89.0000 | 637.0000 | 57.0000 | 5242.0000 | 26402.0000 | 0.0000 | 247.0000 | 1323.0000 | 15568.0000 | 102577.0000 | 569.0000 | 4491.0000 | 500.0000 | 526.0000 | 8522.0000 | 4586.0000 | 4582.0668 | 593.0000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Yemen | 26532.0000 | 1590.0000 | 31045.0000 | 1110837.0000 | 126525.0000 | 52119.0000 | 64136.0000 | 95610.0000 | 30812.0000 | 419051.0000 | 111536.0000 | 27994.0000 | 3718.0000 | 1049.0000 | 1131.0000 | 23871.0000 | 6276.0000 | 17918.0000 | 259045.0000 | 143463.0000 | 53611.0000 | 21095.0000 | 729558.0000 | 234015.0000 | 68939.0000 | 7188.0000 | 12561.0000 | 66731.0000 | 278327.0000 | 29882.0000 | 10522.1795 | 32460.0000 |
| Yugoslavia | 55034.7191 | 73273.9341 | 470807.3588 | 6807332.8674 | 1578014.9903 | 433745.4352 | 560236.0225 | 45163.8532 | 473234.7341 | 977530.0330 | 991378.9154 | 150391.6554 | 41639.5071 | 26844.6712 | 20501.0202 | 53338.0569 | 567017.1386 | 183790.1798 | 1251504.8689 | 381744.5348 | 111808.3970 | 154916.4135 | 1112262.9303 | 3580782.1124 | 199032.6876 | 112404.2037 | 38269.3333 | 173296.6142 | 534199.3708 | 360870.2936 | 113.0000 | 672663.2419 |
| Zaire | 27517.3596 | 36636.9670 | 235403.6794 | 3403666.4337 | 789007.4951 | 216872.7176 | 280118.0112 | 22581.9266 | 236617.3670 | 488765.0165 | 495689.4577 | 75195.8277 | 20819.7536 | 13422.3356 | 10250.5101 | 26669.0285 | 283508.5693 | 91895.0899 | 625752.4345 | 190872.2674 | 55904.1985 | 77458.2067 | 556131.4652 | 1790391.0562 | 99516.3438 | 56202.1019 | 19134.6667 | 86648.3071 | 267099.6854 | 180435.1468 | 293.0000 | 336331.6210 |
| Zambia | 8847.0000 | 2677.0000 | 13473.0000 | 360770.0000 | 59174.0000 | 41751.0000 | 100581.0000 | 159.0000 | 54098.0000 | 348764.0000 | 147640.0000 | 12809.0000 | 933.0000 | 2451.0000 | 73.0000 | 9476.0000 | 1175563.0000 | 30066.0000 | 345854.0000 | 205529.0000 | 28395.0000 | 98886.0000 | 300479.0000 | 198743.0000 | 95913.0000 | 4053.0000 | 9056.0000 | 92915.0000 | 56975.0000 | 29215.0000 | 8053.4258 | 275391.0000 |
| Zimbabwe | 3778.0000 | 1246.0000 | 20017.0000 | 408352.0000 | 71774.0000 | 49952.0000 | 55027.0000 | 625.0000 | 71176.0000 | 140850.0000 | 108691.0000 | 18169.0000 | 2271.0000 | 978.0000 | 1247.0000 | 14718.0000 | 1836042.0000 | 32741.0000 | 326145.0000 | 118728.0000 | 29802.0000 | 41238.0000 | 251875.0000 | 272787.0000 | 66723.0000 | 5764.0000 | 9113.0000 | 65942.0000 | 67207.0000 | 51764.0000 | 4567.0668 | 273844.0000 |
293 rows × 32 columns
afridf = new_df.pivot_table(index = 'Country', values = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'], aggfunc = 'sum')
Nigdf = afridf.loc[['Nigeria']].sum().sort_values(ascending = False)
Nigdf = pd.DataFrame(Nigdf)
Nigdf.columns = ['Number of Deaths']
Nigdf
| Number of Deaths | |
|---|---|
| Diarrheal_diseases | 7449328.0000 |
| Malaria | 6422063.0000 |
| Lower_respiratory_infections | 5917528.0000 |
| Neonatal_disorders | 5262229.0000 |
| Cardiovascular_diseases | 4176488.0000 |
| HIV/AIDS | 2216718.0000 |
| Tuberculosis | 1769390.0000 |
| Digestive_diseases | 1716202.0000 |
| Neoplasms | 1618730.0000 |
| Meningitis | 1520376.0000 |
| Cirrhosis_liver_diseases | 995203.0000 |
| Chronic_Respiratory_diseases | 641714.0000 |
| Diabetes_mellitus | 541020.0000 |
| Maternal_disorders | 525566.0000 |
| Road_injuries | 487695.0000 |
| Chronic_kidney_disease | 464656.0000 |
| Interpersonal_violence | 306846.0000 |
| Nutritional_deficiencies | 286858.0000 |
| Protein-energy_malnutrition | 270470.0000 |
| Alzheimer disease | 241713.0000 |
| Self-harm | 190297.0000 |
| Acute_hepatitis | 119860.0000 |
| Fire_heat_and_hot_substances | 110784.0000 |
| Poisonings | 107604.0000 |
| Drowning | 103723.0000 |
| Conflict_and_terrorism | 78908.0000 |
| Parkinson disease | 66545.0000 |
| Alcohol_use_disorders | 28341.0000 |
| Environmental_heat_and_cold_exposure | 26363.0000 |
| Terrorism | 24070.9436 |
| Drug_use_disorders | 4897.0000 |
| Exposure_to_forces_of_nature | 1899.0000 |
fig = px.bar(Nigdf, 'Number of Deaths', color = 'Number of Deaths', title = 'Names of Death by Dieaseses')
fig.update_layout(font_family="Courier New", font_color="blue", title_font_family="Times New Roman", title_font_color="purple")
fig.show()
new_df[['Year_of_Death','Country', 'Conflict_and_terrorism', 'Terrorism', 'Poisonings', 'HIV/AIDS']].sort_values(by=['HIV/AIDS', 'Year_of_Death'], ascending = False)[:20]
| Year_of_Death | Country | Conflict_and_terrorism | Terrorism | Poisonings | HIV/AIDS | |
|---|---|---|---|---|---|---|
| 8000 | 2004 | World | 70562.0000 | 5743.0000 | 91491.0000 | 1844490.0000 |
| 8001 | 2005 | World | 59555.0000 | 6331.0000 | 92101.0000 | 1833561.0000 |
| 7999 | 2003 | World | 71091.0000 | 3317.0000 | 90675.0000 | 1809961.0000 |
| 8002 | 2006 | World | 74623.0000 | 9380.0000 | 89895.0000 | 1769283.0000 |
| 7998 | 2002 | World | 66446.0000 | 4805.0000 | 89958.0000 | 1747605.0000 |
| 8003 | 2007 | World | 71352.0000 | 12824.0000 | 88513.0000 | 1670624.0000 |
| 7997 | 2001 | World | 67104.0000 | 7729.0000 | 89326.0000 | 1663535.0000 |
| 7996 | 2000 | World | 120087.0000 | 4403.0000 | 88840.0000 | 1560801.0000 |
| 8004 | 2008 | World | 64747.0000 | 9157.0000 | 88731.0000 | 1560729.0000 |
| 44 | 2004 | Africa | 34198.0000 | 349.2359 | 20636.0000 | 1500899.0000 |
| 104 | 2004 | African Union | 34198.0000 | 349.2359 | 20636.0000 | 1500899.0000 |
| 6944 | 2004 | Sub-Saharan Africa - World Bank region | 33559.0000 | 349.2359 | 19571.0000 | 1498836.0000 |
| 74 | 2004 | African Region | 20148.0000 | 349.2359 | 18747.0000 | 1491641.0000 |
| 43 | 2003 | Africa | 39653.0000 | 349.2359 | 20517.0000 | 1482330.0000 |
| 103 | 2003 | African Union | 39653.0000 | 349.2359 | 20517.0000 | 1482330.0000 |
| 6943 | 2003 | Sub-Saharan Africa - World Bank region | 38644.0000 | 349.2359 | 19439.0000 | 1480387.0000 |
| 45 | 2005 | Africa | 19808.0000 | 349.2359 | 20798.0000 | 1479893.0000 |
| 105 | 2005 | African Union | 19808.0000 | 349.2359 | 20798.0000 | 1479893.0000 |
| 6945 | 2005 | Sub-Saharan Africa - World Bank region | 19353.0000 | 349.2359 | 19748.0000 | 1477720.0000 |
| 73 | 2003 | African Region | 30846.0000 | 349.2359 | 18612.0000 | 1473615.0000 |
y_Nig = new_df[new_df['Country']=='Nigeria']
y_Nig.groupby('Year_of_Death')['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'].sum()
C:\Users\user\AppData\Local\Temp/ipykernel_8456/1325130242.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
| Meningitis | Neoplasms | Fire_heat_and_hot_substances | Malaria | Drowning | Interpersonal_violence | HIV/AIDS | Drug_use_disorders | Tuberculosis | Road_injuries | Maternal_disorders | Lower_respiratory_infections | Neonatal_disorders | Alcohol_use_disorders | Exposure_to_forces_of_nature | Diarrheal_diseases | Environmental_heat_and_cold_exposure | Nutritional_deficiencies | Self-harm | Conflict_and_terrorism | Diabetes_mellitus | Poisonings | Protein-energy_malnutrition | Terrorism | Cardiovascular_diseases | Chronic_kidney_disease | Chronic_Respiratory_diseases | Cirrhosis_liver_diseases | Digestive_diseases | Acute_hepatitis | Alzheimer disease | Parkinson disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Year_of_Death | ||||||||||||||||||||||||||||||||
| 1990 | 40226.0000 | 34236.0000 | 2876.0000 | 148931.0000 | 2887.0000 | 6579.0000 | 7723.0000 | 95.0000 | 59421.0000 | 11832.0000 | 11879.0000 | 169472.0000 | 121027.0000 | 743.0000 | 0.0000 | 284419.0000 | 728.0000 | 10236.0000 | 4330.0000 | 81.0000 | 12194.0000 | 2937.0000 | 9769.0000 | 349.2359 | 118057.0000 | 11660.0000 | 17749.0000 | 25713.0000 | 43375.0000 | 4292.0000 | 4984.0000 | 1431.0000 |
| 1991 | 41349.0000 | 35172.0000 | 2948.0000 | 157502.0000 | 2977.0000 | 7140.0000 | 11761.0000 | 100.0000 | 60481.0000 | 12185.0000 | 12398.0000 | 174686.0000 | 125598.0000 | 754.0000 | 0.0000 | 297403.0000 | 744.0000 | 10517.0000 | 4418.0000 | 13.0000 | 12561.0000 | 2908.0000 | 10039.0000 | 10.0000 | 120203.0000 | 11928.0000 | 17994.0000 | 26125.0000 | 44145.0000 | 4475.0000 | 5107.0000 | 1460.0000 |
| 1992 | 42711.0000 | 36234.0000 | 3037.0000 | 165722.0000 | 3071.0000 | 7951.0000 | 17130.0000 | 105.0000 | 62090.0000 | 12663.0000 | 12918.0000 | 180284.0000 | 130522.0000 | 771.0000 | 0.0000 | 281303.0000 | 782.0000 | 10624.0000 | 4566.0000 | 147.0000 | 13022.0000 | 3007.0000 | 10141.0000 | 135.0000 | 123006.0000 | 12257.0000 | 18313.0000 | 26664.0000 | 45145.0000 | 4628.0000 | 5255.0000 | 1496.0000 |
| 1993 | 44166.0000 | 37283.0000 | 3133.0000 | 173695.0000 | 3165.0000 | 8438.0000 | 23871.0000 | 110.0000 | 63446.0000 | 13082.0000 | 13396.0000 | 185946.0000 | 135700.0000 | 782.0000 | 0.0000 | 276528.0000 | 781.0000 | 10693.0000 | 4694.0000 | 3.0000 | 13453.0000 | 3107.0000 | 10210.0000 | 349.2359 | 125642.0000 | 12536.0000 | 18594.0000 | 27081.0000 | 46006.0000 | 4666.0000 | 5395.0000 | 1528.0000 |
| 1994 | 45460.0000 | 38162.0000 | 3205.0000 | 180588.0000 | 3256.0000 | 7467.0000 | 31881.0000 | 113.0000 | 65194.0000 | 13421.0000 | 13962.0000 | 192168.0000 | 140521.0000 | 788.0000 | 30.0000 | 273916.0000 | 800.0000 | 11053.0000 | 4803.0000 | 90.0000 | 13851.0000 | 3189.0000 | 10552.0000 | 15.0000 | 127914.0000 | 12756.0000 | 18837.0000 | 27378.0000 | 46688.0000 | 4825.0000 | 5540.0000 | 1557.0000 |
| 1995 | 46492.0000 | 38843.0000 | 3265.0000 | 186263.0000 | 3313.0000 | 7634.0000 | 40865.0000 | 115.0000 | 67625.0000 | 13667.0000 | 14061.0000 | 198897.0000 | 144810.0000 | 793.0000 | 0.0000 | 271658.0000 | 820.0000 | 11401.0000 | 4903.0000 | 9.0000 | 14172.0000 | 3252.0000 | 10874.0000 | 1.0000 | 129482.0000 | 12887.0000 | 19031.0000 | 27473.0000 | 47083.0000 | 5022.0000 | 5654.0000 | 1578.0000 |
| 1996 | 59006.0000 | 39405.0000 | 3307.0000 | 191437.0000 | 3367.0000 | 7900.0000 | 50405.0000 | 117.0000 | 68778.0000 | 13879.0000 | 14618.0000 | 203492.0000 | 148866.0000 | 795.0000 | 0.0000 | 274988.0000 | 832.0000 | 11568.0000 | 5023.0000 | 83.0000 | 14434.0000 | 3296.0000 | 11029.0000 | 24.0000 | 132523.0000 | 13000.0000 | 19112.0000 | 27460.0000 | 47296.0000 | 5067.0000 | 5754.0000 | 1592.0000 |
| 1997 | 48278.0000 | 40298.0000 | 3365.0000 | 197467.0000 | 3417.0000 | 8586.0000 | 59969.0000 | 119.0000 | 69098.0000 | 14199.0000 | 15300.0000 | 206728.0000 | 153247.0000 | 803.0000 | 0.0000 | 272445.0000 | 844.0000 | 11913.0000 | 5234.0000 | 452.0000 | 14804.0000 | 3362.0000 | 11351.0000 | 107.0000 | 135221.0000 | 13176.0000 | 19311.0000 | 27746.0000 | 47939.0000 | 5178.0000 | 5894.0000 | 1614.0000 |
| 1998 | 50009.0000 | 41305.0000 | 3435.0000 | 202843.0000 | 3478.0000 | 8500.0000 | 71604.0000 | 122.0000 | 69735.0000 | 14574.0000 | 16126.0000 | 209694.0000 | 157553.0000 | 812.0000 | 15.0000 | 272459.0000 | 857.0000 | 12155.0000 | 5472.0000 | 943.0000 | 15193.0000 | 3438.0000 | 11574.0000 | 9.0000 | 138002.0000 | 13336.0000 | 19542.0000 | 28018.0000 | 48666.0000 | 5246.0000 | 6041.0000 | 1637.0000 |
| 1999 | 50499.0000 | 42312.0000 | 3489.0000 | 207571.0000 | 3528.0000 | 9369.0000 | 82999.0000 | 124.0000 | 70661.0000 | 14916.0000 | 16891.0000 | 212981.0000 | 162025.0000 | 820.0000 | 189.0000 | 273760.0000 | 871.0000 | 12206.0000 | 5706.0000 | 2692.0000 | 15568.0000 | 3503.0000 | 11614.0000 | 134.0000 | 140664.0000 | 13492.0000 | 19824.0000 | 28428.0000 | 49645.0000 | 5219.0000 | 6186.0000 | 1661.0000 |
| 2000 | 51685.0000 | 43405.0000 | 3541.0000 | 212123.0000 | 3554.0000 | 10596.0000 | 91616.0000 | 124.0000 | 71501.0000 | 15229.0000 | 17263.0000 | 215544.0000 | 166039.0000 | 830.0000 | 36.0000 | 273848.0000 | 884.0000 | 11924.0000 | 5876.0000 | 3553.0000 | 15961.0000 | 3561.0000 | 11334.0000 | 0.0000 | 142398.0000 | 13777.0000 | 20129.0000 | 28957.0000 | 50641.0000 | 5179.0000 | 6395.0000 | 1719.0000 |
| 2001 | 52027.0000 | 44569.0000 | 3607.0000 | 221139.0000 | 3557.0000 | 10783.0000 | 99303.0000 | 127.0000 | 71286.0000 | 15536.0000 | 17960.0000 | 216216.0000 | 170248.0000 | 846.0000 | 200.0000 | 272987.0000 | 896.0000 | 11977.0000 | 6036.0000 | 1155.0000 | 16317.0000 | 3598.0000 | 11373.0000 | 3.0000 | 141888.0000 | 14129.0000 | 20422.0000 | 29733.0000 | 51745.0000 | 5162.0000 | 6595.0000 | 1782.0000 |
| 2002 | 53195.0000 | 46497.0000 | 3664.0000 | 226408.0000 | 3617.0000 | 9492.0000 | 104701.0000 | 132.0000 | 70036.0000 | 16012.0000 | 18725.0000 | 216774.0000 | 175409.0000 | 869.0000 | 0.0000 | 268973.0000 | 966.0000 | 11629.0000 | 6198.0000 | 2101.0000 | 16937.0000 | 3674.0000 | 11038.0000 | 28.0000 | 142175.0000 | 14684.0000 | 20931.0000 | 30913.0000 | 53662.0000 | 4836.0000 | 6962.0000 | 1890.0000 |
| 2003 | 53916.0000 | 48197.0000 | 3714.0000 | 235380.0000 | 3690.0000 | 9554.0000 | 108081.0000 | 138.0000 | 68360.0000 | 16341.0000 | 19295.0000 | 215409.0000 | 179486.0000 | 890.0000 | 16.0000 | 262567.0000 | 908.0000 | 11390.0000 | 6325.0000 | 1027.0000 | 17415.0000 | 3712.0000 | 10806.0000 | 28.0000 | 140179.0000 | 15123.0000 | 21239.0000 | 31846.0000 | 55084.0000 | 4508.0000 | 7262.0000 | 1964.0000 |
| 2004 | 54092.0000 | 50181.0000 | 3778.0000 | 236937.0000 | 3658.0000 | 11327.0000 | 109749.0000 | 144.0000 | 65773.0000 | 16690.0000 | 19914.0000 | 213336.0000 | 182540.0000 | 915.0000 | 94.0000 | 256236.0000 | 907.0000 | 10589.0000 | 6455.0000 | 1445.0000 | 17873.0000 | 3758.0000 | 10018.0000 | 41.0000 | 138269.0000 | 15493.0000 | 21580.0000 | 32887.0000 | 56738.0000 | 4210.0000 | 7635.0000 | 2064.0000 |
| 2005 | 54266.0000 | 51932.0000 | 3811.0000 | 243827.0000 | 3679.0000 | 9720.0000 | 109023.0000 | 150.0000 | 61754.0000 | 16867.0000 | 20274.0000 | 208505.0000 | 185687.0000 | 931.0000 | 60.0000 | 248877.0000 | 897.0000 | 9651.0000 | 6542.0000 | 360.0000 | 18145.0000 | 3769.0000 | 9105.0000 | 19.0000 | 136056.0000 | 15586.0000 | 21792.0000 | 33850.0000 | 58188.0000 | 3948.0000 | 7992.0000 | 2163.0000 |
| 2006 | 56924.0000 | 53931.0000 | 3986.0000 | 257505.0000 | 3837.0000 | 9940.0000 | 105000.0000 | 155.0000 | 58881.0000 | 17447.0000 | 20344.0000 | 210683.0000 | 189061.0000 | 947.0000 | 40.0000 | 254013.0000 | 909.0000 | 9620.0000 | 6658.0000 | 589.0000 | 18539.0000 | 3916.0000 | 9063.0000 | 254.0000 | 135125.0000 | 15873.0000 | 22119.0000 | 34803.0000 | 59910.0000 | 3810.0000 | 8385.0000 | 2279.0000 |
| 2007 | 57314.0000 | 55609.0000 | 4142.0000 | 267368.0000 | 3883.0000 | 10074.0000 | 100730.0000 | 161.0000 | 55797.0000 | 17720.0000 | 20349.0000 | 208401.0000 | 192018.0000 | 961.0000 | 91.0000 | 253568.0000 | 909.0000 | 9382.0000 | 6814.0000 | 628.0000 | 18828.0000 | 3954.0000 | 8823.0000 | 82.0000 | 133656.0000 | 16026.0000 | 22170.0000 | 35595.0000 | 61219.0000 | 3678.0000 | 8602.0000 | 2342.0000 |
| 2008 | 56276.0000 | 57021.0000 | 3986.0000 | 280604.0000 | 3745.0000 | 10124.0000 | 95773.0000 | 165.0000 | 52577.0000 | 17507.0000 | 19930.0000 | 202257.0000 | 194666.0000 | 966.0000 | 0.0000 | 246594.0000 | 892.0000 | 8850.0000 | 6883.0000 | 913.0000 | 19065.0000 | 3870.0000 | 8301.0000 | 72.0000 | 133744.0000 | 16136.0000 | 22244.0000 | 35955.0000 | 61813.0000 | 3501.0000 | 8938.0000 | 2447.0000 |
| 2009 | 59349.0000 | 59145.0000 | 4004.0000 | 276715.0000 | 3733.0000 | 10223.0000 | 89799.0000 | 172.0000 | 51184.0000 | 17658.0000 | 20001.0000 | 199250.0000 | 197097.0000 | 987.0000 | 31.0000 | 243669.0000 | 895.0000 | 8663.0000 | 7122.0000 | 2668.0000 | 19485.0000 | 3857.0000 | 8110.0000 | 316.0000 | 135318.0000 | 16430.0000 | 22529.0000 | 36699.0000 | 62992.0000 | 3400.0000 | 9213.0000 | 2533.0000 |
| 2010 | 55105.0000 | 61627.0000 | 3994.0000 | 266638.0000 | 3663.0000 | 11174.0000 | 87236.0000 | 182.0000 | 49386.0000 | 17801.0000 | 19991.0000 | 194723.0000 | 199507.0000 | 1017.0000 | 40.0000 | 240708.0000 | 893.0000 | 8385.0000 | 7346.0000 | 1497.0000 | 19988.0000 | 4029.0000 | 7832.0000 | 117.0000 | 137026.0000 | 16694.0000 | 22820.0000 | 37704.0000 | 64449.0000 | 3300.0000 | 9527.0000 | 2623.0000 |
| 2011 | 53692.0000 | 64551.0000 | 3960.0000 | 254072.0000 | 3509.0000 | 11819.0000 | 86006.0000 | 195.0000 | 47775.0000 | 17997.0000 | 20254.0000 | 189770.0000 | 201486.0000 | 1054.0000 | 185.0000 | 228309.0000 | 884.0000 | 7995.0000 | 7517.0000 | 1650.0000 | 20621.0000 | 3782.0000 | 7449.0000 | 447.0000 | 139912.0000 | 17019.0000 | 23125.0000 | 38456.0000 | 65727.0000 | 3205.0000 | 9874.0000 | 2719.0000 |
| 2012 | 52126.0000 | 67786.0000 | 4009.0000 | 239886.0000 | 3563.0000 | 11269.0000 | 82793.0000 | 208.0000 | 47582.0000 | 18358.0000 | 20112.0000 | 189640.0000 | 203428.0000 | 1091.0000 | 378.0000 | 221382.0000 | 893.0000 | 7943.0000 | 7612.0000 | 3091.0000 | 21336.0000 | 3808.0000 | 7389.0000 | 1508.0000 | 143645.0000 | 17483.0000 | 23577.0000 | 39274.0000 | 67301.0000 | 3128.0000 | 10209.0000 | 2828.0000 |
| 2013 | 52141.0000 | 70387.0000 | 4042.0000 | 227310.0000 | 3458.0000 | 12037.0000 | 77539.0000 | 220.0000 | 48787.0000 | 18610.0000 | 19685.0000 | 191077.0000 | 204573.0000 | 1123.0000 | 19.0000 | 215679.0000 | 910.0000 | 7542.0000 | 7613.0000 | 6260.0000 | 21835.0000 | 3809.0000 | 6984.0000 | 2014.0000 | 146744.0000 | 17827.0000 | 23880.0000 | 39606.0000 | 68184.0000 | 3040.0000 | 10472.0000 | 2917.0000 |
| 2014 | 52114.0000 | 72209.0000 | 4049.0000 | 218689.0000 | 3443.0000 | 12925.0000 | 74814.0000 | 229.0000 | 50413.0000 | 18723.0000 | 19240.0000 | 192397.0000 | 203887.0000 | 1141.0000 | 15.0000 | 210178.0000 | 934.0000 | 7149.0000 | 7552.0000 | 14156.0000 | 22171.0000 | 3789.0000 | 6593.0000 | 7781.0000 | 148499.0000 | 18290.0000 | 23860.0000 | 39342.0000 | 68129.0000 | 2935.0000 | 10623.0000 | 2945.0000 |
| 2015 | 48484.0000 | 73971.0000 | 4069.0000 | 204672.0000 | 3399.0000 | 12648.0000 | 75930.0000 | 236.0000 | 50844.0000 | 19053.0000 | 18644.0000 | 192033.0000 | 203212.0000 | 1145.0000 | 94.0000 | 203706.0000 | 947.0000 | 7025.0000 | 7499.0000 | 14635.0000 | 22565.0000 | 3779.0000 | 6467.0000 | 5559.0000 | 153963.0000 | 18731.0000 | 23953.0000 | 38674.0000 | 67489.0000 | 2807.0000 | 10918.0000 | 3025.0000 |
| 2016 | 49572.0000 | 75842.0000 | 4157.0000 | 186482.0000 | 3505.0000 | 13090.0000 | 77599.0000 | 246.0000 | 50424.0000 | 19370.0000 | 18441.0000 | 191915.0000 | 202317.0000 | 1164.0000 | 46.0000 | 200794.0000 | 962.0000 | 6884.0000 | 7624.0000 | 5064.0000 | 22964.0000 | 3834.0000 | 6317.0000 | 2165.0000 | 155266.0000 | 19144.0000 | 24038.0000 | 38874.0000 | 68094.0000 | 2761.0000 | 11041.0000 | 3053.0000 |
| 2017 | 48790.0000 | 77596.0000 | 4104.0000 | 176183.0000 | 3364.0000 | 13436.0000 | 79121.0000 | 255.0000 | 48699.0000 | 19319.0000 | 18156.0000 | 184438.0000 | 199123.0000 | 1183.0000 | 20.0000 | 198476.0000 | 954.0000 | 6490.0000 | 7706.0000 | 5077.0000 | 23373.0000 | 3767.0000 | 5934.0000 | 1805.0000 | 157966.0000 | 19428.0000 | 24189.0000 | 39654.0000 | 69254.0000 | 2699.0000 | 11394.0000 | 3130.0000 |
| 2018 | 46198.0000 | 79466.0000 | 4090.0000 | 187000.0000 | 3254.0000 | 14093.0000 | 81427.0000 | 264.0000 | 46524.0000 | 18531.0000 | 17789.0000 | 177834.0000 | 197179.0000 | 1199.0000 | 300.0000 | 188747.0000 | 937.0000 | 5908.0000 | 7800.0000 | 5440.0000 | 23877.0000 | 3699.0000 | 5385.0000 | 349.2359 | 160449.0000 | 19710.0000 | 24304.0000 | 39914.0000 | 69518.0000 | 2595.0000 | 11753.0000 | 3234.0000 |
| 2019 | 44914.0000 | 81558.0000 | 4017.0000 | 191106.0000 | 3153.0000 | 12958.0000 | 82270.0000 | 274.0000 | 45278.0000 | 18508.0000 | 17650.0000 | 172978.0000 | 195397.0000 | 1221.0000 | 0.0000 | 181138.0000 | 927.0000 | 5496.0000 | 7970.0000 | 3086.0000 | 24473.0000 | 3640.0000 | 4996.0000 | 349.2359 | 163496.0000 | 20045.0000 | 24506.0000 | 40381.0000 | 70077.0000 | 2540.0000 | 12113.0000 | 3334.0000 |
new_df[new_df['Country']== 'Nigeria'].sum()
Country NigeriaNigeriaNigeriaNigeriaNigeriaNigeriaNige... Year_of_Death 60135 Meningitis 1520376.0000 Neoplasms 1618730.0000 Fire_heat_and_hot_substances 110784.0000 Malaria 6422063.0000 Drowning 103723.0000 Interpersonal_violence 306846.0000 HIV/AIDS 2216718.0000 Drug_use_disorders 4897.0000 Tuberculosis 1769390.0000 Road_injuries 487695.0000 Maternal_disorders 525566.0000 Lower_respiratory_infections 5917528.0000 Neonatal_disorders 5262229.0000 Alcohol_use_disorders 28341.0000 Exposure_to_forces_of_nature 1899.0000 Diarrheal_diseases 7449328.0000 Environmental_heat_and_cold_exposure 26363.0000 Nutritional_deficiencies 286858.0000 Self-harm 190297.0000 Conflict_and_terrorism 78908.0000 Diabetes_mellitus 541020.0000 Poisonings 107604.0000 Protein-energy_malnutrition 270470.0000 Terrorism 24070.9436 Cardiovascular_diseases 4176488.0000 Chronic_kidney_disease 464656.0000 Chronic_Respiratory_diseases 641714.0000 Cirrhosis_liver_diseases 995203.0000 Digestive_diseases 1716202.0000 Acute_hepatitis 119860.0000 Alzheimer disease 241713.0000 Parkinson disease 66545.0000 dtype: object
y_Nig.head().sort_values(by = 'HIV/AIDS', ascending = False)
| Country | Year_of_Death | Meningitis | Neoplasms | Fire_heat_and_hot_substances | Malaria | Drowning | Interpersonal_violence | HIV/AIDS | Drug_use_disorders | Tuberculosis | Road_injuries | Maternal_disorders | Lower_respiratory_infections | Neonatal_disorders | Alcohol_use_disorders | Exposure_to_forces_of_nature | Diarrheal_diseases | Environmental_heat_and_cold_exposure | Nutritional_deficiencies | Self-harm | Conflict_and_terrorism | Diabetes_mellitus | Poisonings | Protein-energy_malnutrition | Terrorism | Cardiovascular_diseases | Chronic_kidney_disease | Chronic_Respiratory_diseases | Cirrhosis_liver_diseases | Digestive_diseases | Acute_hepatitis | Alzheimer disease | Parkinson disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5135 | Nigeria | 2007 | 57314.0000 | 55609.0000 | 4142.0000 | 267368.0000 | 3883.0000 | 10074.0000 | 100730.0000 | 161.0000 | 55797.0000 | 17720.0000 | 20349.0000 | 208401.0000 | 192018.0000 | 961.0000 | 91.0000 | 253568.0000 | 909.0000 | 9382.0000 | 6814.0000 | 628.0000 | 18828.0000 | 3954.0000 | 8823.0000 | 82.0000 | 133656.0000 | 16026.0000 | 22170.0000 | 35595.0000 | 61219.0000 | 3678.0000 | 8602.0000 | 2342.0000 |
| 5136 | Nigeria | 2008 | 56276.0000 | 57021.0000 | 3986.0000 | 280604.0000 | 3745.0000 | 10124.0000 | 95773.0000 | 165.0000 | 52577.0000 | 17507.0000 | 19930.0000 | 202257.0000 | 194666.0000 | 966.0000 | 0.0000 | 246594.0000 | 892.0000 | 8850.0000 | 6883.0000 | 913.0000 | 19065.0000 | 3870.0000 | 8301.0000 | 72.0000 | 133744.0000 | 16136.0000 | 22244.0000 | 35955.0000 | 61813.0000 | 3501.0000 | 8938.0000 | 2447.0000 |
| 5137 | Nigeria | 2009 | 59349.0000 | 59145.0000 | 4004.0000 | 276715.0000 | 3733.0000 | 10223.0000 | 89799.0000 | 172.0000 | 51184.0000 | 17658.0000 | 20001.0000 | 199250.0000 | 197097.0000 | 987.0000 | 31.0000 | 243669.0000 | 895.0000 | 8663.0000 | 7122.0000 | 2668.0000 | 19485.0000 | 3857.0000 | 8110.0000 | 316.0000 | 135318.0000 | 16430.0000 | 22529.0000 | 36699.0000 | 62992.0000 | 3400.0000 | 9213.0000 | 2533.0000 |
| 5139 | Nigeria | 2016 | 49572.0000 | 75842.0000 | 4157.0000 | 186482.0000 | 3505.0000 | 13090.0000 | 77599.0000 | 246.0000 | 50424.0000 | 19370.0000 | 18441.0000 | 191915.0000 | 202317.0000 | 1164.0000 | 46.0000 | 200794.0000 | 962.0000 | 6884.0000 | 7624.0000 | 5064.0000 | 22964.0000 | 3834.0000 | 6317.0000 | 2165.0000 | 155266.0000 | 19144.0000 | 24038.0000 | 38874.0000 | 68094.0000 | 2761.0000 | 11041.0000 | 3053.0000 |
| 5138 | Nigeria | 2013 | 52141.0000 | 70387.0000 | 4042.0000 | 227310.0000 | 3458.0000 | 12037.0000 | 77539.0000 | 220.0000 | 48787.0000 | 18610.0000 | 19685.0000 | 191077.0000 | 204573.0000 | 1123.0000 | 19.0000 | 215679.0000 | 910.0000 | 7542.0000 | 7613.0000 | 6260.0000 | 21835.0000 | 3809.0000 | 6984.0000 | 2014.0000 | 146744.0000 | 17827.0000 | 23880.0000 | 39606.0000 | 68184.0000 | 3040.0000 | 10472.0000 | 2917.0000 |
sorted_y_Nig = y_Nig.head().sort_values(by = 'HIV/AIDS', ascending = False)
fig = px.bar(sorted_y_Nig, x = 'Year_of_Death', y = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'])
fig.show()
y_Nig.head().sort_values(by = 'HIV/AIDS', ascending = False)
| Country | Year_of_Death | Meningitis | Neoplasms | Fire_heat_and_hot_substances | Malaria | Drowning | Interpersonal_violence | HIV/AIDS | Drug_use_disorders | Tuberculosis | Road_injuries | Maternal_disorders | Lower_respiratory_infections | Neonatal_disorders | Alcohol_use_disorders | Exposure_to_forces_of_nature | Diarrheal_diseases | Environmental_heat_and_cold_exposure | Nutritional_deficiencies | Self-harm | Conflict_and_terrorism | Diabetes_mellitus | Poisonings | Protein-energy_malnutrition | Terrorism | Cardiovascular_diseases | Chronic_kidney_disease | Chronic_Respiratory_diseases | Cirrhosis_liver_diseases | Digestive_diseases | Acute_hepatitis | Alzheimer disease | Parkinson disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5135 | Nigeria | 2007 | 57314.0000 | 55609.0000 | 4142.0000 | 267368.0000 | 3883.0000 | 10074.0000 | 100730.0000 | 161.0000 | 55797.0000 | 17720.0000 | 20349.0000 | 208401.0000 | 192018.0000 | 961.0000 | 91.0000 | 253568.0000 | 909.0000 | 9382.0000 | 6814.0000 | 628.0000 | 18828.0000 | 3954.0000 | 8823.0000 | 82.0000 | 133656.0000 | 16026.0000 | 22170.0000 | 35595.0000 | 61219.0000 | 3678.0000 | 8602.0000 | 2342.0000 |
| 5136 | Nigeria | 2008 | 56276.0000 | 57021.0000 | 3986.0000 | 280604.0000 | 3745.0000 | 10124.0000 | 95773.0000 | 165.0000 | 52577.0000 | 17507.0000 | 19930.0000 | 202257.0000 | 194666.0000 | 966.0000 | 0.0000 | 246594.0000 | 892.0000 | 8850.0000 | 6883.0000 | 913.0000 | 19065.0000 | 3870.0000 | 8301.0000 | 72.0000 | 133744.0000 | 16136.0000 | 22244.0000 | 35955.0000 | 61813.0000 | 3501.0000 | 8938.0000 | 2447.0000 |
| 5137 | Nigeria | 2009 | 59349.0000 | 59145.0000 | 4004.0000 | 276715.0000 | 3733.0000 | 10223.0000 | 89799.0000 | 172.0000 | 51184.0000 | 17658.0000 | 20001.0000 | 199250.0000 | 197097.0000 | 987.0000 | 31.0000 | 243669.0000 | 895.0000 | 8663.0000 | 7122.0000 | 2668.0000 | 19485.0000 | 3857.0000 | 8110.0000 | 316.0000 | 135318.0000 | 16430.0000 | 22529.0000 | 36699.0000 | 62992.0000 | 3400.0000 | 9213.0000 | 2533.0000 |
| 5139 | Nigeria | 2016 | 49572.0000 | 75842.0000 | 4157.0000 | 186482.0000 | 3505.0000 | 13090.0000 | 77599.0000 | 246.0000 | 50424.0000 | 19370.0000 | 18441.0000 | 191915.0000 | 202317.0000 | 1164.0000 | 46.0000 | 200794.0000 | 962.0000 | 6884.0000 | 7624.0000 | 5064.0000 | 22964.0000 | 3834.0000 | 6317.0000 | 2165.0000 | 155266.0000 | 19144.0000 | 24038.0000 | 38874.0000 | 68094.0000 | 2761.0000 | 11041.0000 | 3053.0000 |
| 5138 | Nigeria | 2013 | 52141.0000 | 70387.0000 | 4042.0000 | 227310.0000 | 3458.0000 | 12037.0000 | 77539.0000 | 220.0000 | 48787.0000 | 18610.0000 | 19685.0000 | 191077.0000 | 204573.0000 | 1123.0000 | 19.0000 | 215679.0000 | 910.0000 | 7542.0000 | 7613.0000 | 6260.0000 | 21835.0000 | 3809.0000 | 6984.0000 | 2014.0000 | 146744.0000 | 17827.0000 | 23880.0000 | 39606.0000 | 68184.0000 | 3040.0000 | 10472.0000 | 2917.0000 |
fig = px.bar(y_Nig, x = 'Year_of_Death', y = ['Meningitis', 'Neoplasms',
'Fire_heat_and_hot_substances', 'Malaria', 'Drowning',
'Interpersonal_violence', 'HIV/AIDS', 'Drug_use_disorders',
'Tuberculosis', 'Road_injuries', 'Maternal_disorders',
'Lower_respiratory_infections', 'Neonatal_disorders',
'Alcohol_use_disorders', 'Exposure_to_forces_of_nature',
'Diarrheal_diseases', 'Environmental_heat_and_cold_exposure',
'Nutritional_deficiencies', 'Self-harm', 'Conflict_and_terrorism',
'Diabetes_mellitus', 'Poisonings', 'Protein-energy_malnutrition',
'Terrorism', 'Cardiovascular_diseases', 'Chronic_kidney_disease',
'Chronic_Respiratory_diseases', 'Cirrhosis_liver_diseases',
'Digestive_diseases', 'Acute_hepatitis', 'Alzheimer disease',
'Parkinson disease'])
fig.show()
#The end. Thanks for reading!!!